{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"FriendliAI analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/friendliai","json_url":"https://onlylabs.fyi/analysis/friendliai/evidence.json","generated_at":"2026-06-11T12:34:00.996Z","org":{"slug":"friendliai","name":"FriendliAI","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/friendliai"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":86,"web":0,"evidence":88,"signal_desks":{"hiring":20,"forks":12,"releases":16,"talking":0,"repos":12},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"Account Executive","date":"2026-06-11T07:04:02.061331+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/e5c4337b-338d-43e6-9bda-b82178acc3d1","signal_url":null,"signal_json_url":null,"text":"# Account Executive\n\nTeam: Sales\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-06-10T20:55:54.847+00:00\n\nAbout the job\n\nAs a GTM professional at FriendliAI, you will be instrumental in driving our market expansion by securing high-value enterprise accounts and building strategic relationships with leading AI-forward companies. You'll focus on helping enterprises leverage FriendliAI's inference serving platform to achieve transformative cost savings and performance improvements in their AI deployments.\n\nEnvironments\n\n- Highly competitive base salary plus uncapped commission structure tied to enterprise deal value\n\n- Equity in a rapidly growing AI infrastructure company\n\n- Comprehensive benefits package\n\n- Collaborative environment with exposure to cutting-edge AI technologies\n\nKey Responsibilities\n\n- Own the end-to-end sales process with enterprises, from generating pipeline to closing high-value, strategic enterprise deals focused on AI inference and serving solutions\n\n- Identify and capitalize on growth opportunities within existing accounts, becoming a trusted advisor for AI infrastructure optimization\n\n- Manage technical POCs that demonstrate FriendliAI's superior performance and cost advantages, collaborating with internal engineering teams and enterprise clients\n\n- Consistently generate and manage a robust pipeline by identifying enterprises with significant inference workloads and positioning FriendliAI's solutions strategically\n\n- Lead tech community engagements, representing FriendliAI at premier AI/ML conferences, meetups, and industry events (AI Engineer World's Fair, AWS re:Invent, etc.)\n\n- Cultivate strategic relationships with AI engineers, ML platform leaders, and technical decision-makers at target enterprises through community involvement\n\n- Organize technical events; workshops, technical demos, and meetups showcasing FriendliAI\n\n- Collaborate on technical content, case studies, and speaking opportunities that establish FriendliAI's market leadership\n\n- Bring the voice of enterprise customers into product discussions, ensuring FriendliAI's roadmap aligns with market needs and competitive"},{"ref":"P2","kind":"page","title":"friendliai/lm-evaluation-harness repository metadata","date":"2026-06-11T04:19:25.409467+00:00","date_source":null,"source_url":"https://github.com/friendliai/lm-evaluation-harness","signal_url":null,"signal_json_url":null,"text":"# friendliai/lm-evaluation-harness\n\nDescription: A framework for few-shot evaluation of autoregressive language models.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 1\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-08-18T07:03:21Z\n\nPushed: 2025-01-02T01:29:12Z\n\nDefault branch: periflow-eval-master\n\nFork: yes\n\nParent repository: EleutherAI/lm-evaluation-harness\n\nArchived: no\n\nREADME:\n# PeriFlow evaluation harness\n\n## Overview\n- We only support subset of lm-evaluation-harness. Please check [periflow-supported-task-table](periflow_supported_task_table.md).\n\n## Install\n\nTo install the `lm-eval` refactor branch from the github repository, run:\n\n```bash\ngit clone https://github.com/friendliai/lm-evaluation-harness.git\ncd lm-evaluation-harness\npip install -e .\n```\n\nTo install additional multilingual tokenization and text segmentation packages, you must install the package with the `multilingual` extra:\n\n```bash\npip install -e \".[multilingual]\"\n```\n\nTo install the package with all extras, run\n```bash\npip install -e \".[all]\"\n```\n\n## Evaluation Command\n### 1. Sequential Request Evaluation\n```bash\npython main.py \\\n--model periflow \\\n--model_args model_name_or_path {model_name_of_path from huggingface hub},req_url={engine request url} \\\n--tasks {evaluation tas} \\\n--num_fewshot {number of fewshot samples} # optional. without this option, num_fewshot=0\n--no_cache # optional. without this option, if evaluation result is existing, then skip the evaluation process, return cached results.\n--average_acc_tasks # only for mmlu tasks. In lm-evaluation-harness, mmlu dataset contains a lots of seperated datasets. Using this option, the average acc of all seperated datsets is added in result table.\n```\n\n### 2. Async Request Evaluation\n```bash\npython main.py \\\n--model periflow_async \\\n--model_args model_name_or_path {model_name_of_path from huggingface hub},req_url={engine request url} \\\n--tasks {evaluation tas} \\\n--num_fewshot {number of fewshot samples} # optional. without this option, num_fewshot=0\n--no_cache # optional. without this option, if evaluation result is existing, then skip the evaluation process, return cached results.\n--average_acc_tasks # only for mmlu tasks. In lm-evaluation-harnes"},{"ref":"P3","kind":"page","title":"friendliai/langchain repository metadata","date":"2026-06-11T04:19:25.149191+00:00","date_source":null,"source_url":"https://github.com/friendliai/langchain","signal_url":null,"signal_json_url":null,"text":"# friendliai/langchain\n\nDescription: 🦜🔗 Build context-aware reasoning applications\n\nLanguage: Jupyter Notebook\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-02-15T05:26:45Z\n\nPushed: 2025-04-05T21:25:43Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: langchain-ai/langchain\n\nArchived: no\n\nREADME:\n<picture>\n<source media=\"(prefers-color-scheme: light)\" srcset=\"docs/static/img/logo-dark.svg\">\n<source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/static/img/logo-light.svg\">\n<img alt=\"LangChain Logo\" src=\"docs/static/img/logo-dark.svg\" width=\"80%\">\n</picture>\n\n<div>\n<br>\n</div>\n\n[![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/releases)\n[![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)\n[![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-core?style=flat-square)](https://pypistats.org/packages/langchain-core)\n[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain)\n[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/issues)\n[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)\n[<img src=\"https://github.com/codespaces/badge.svg\" title=\"Open in Github Codespace\" width=\"150\" height=\"20\">](https://codespaces.new/langchain-ai/langchain)\n[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)\n\n> [!NOTE]\n> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/lang"},{"ref":"P4","kind":"page","title":"friendliai/llama_index repository metadata","date":"2026-06-11T04:19:25.104997+00:00","date_source":null,"source_url":"https://github.com/friendliai/llama_index","signal_url":null,"signal_json_url":null,"text":"# friendliai/llama_index\n\nDescription: LlamaIndex (formerly GPT Index) is a data framework for your LLM applications\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-02-23T04:37:20Z\n\nPushed: 2025-04-05T21:25:29Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: run-llama/llama_index\n\nArchived: no\n\nREADME:\n# 🗂️ LlamaIndex 🦙\n\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-index)](https://pypi.org/project/llama-index/)\n[![GitHub contributors](https://img.shields.io/github/contributors/jerryjliu/llama_index)](https://github.com/jerryjliu/llama_index/graphs/contributors)\n[![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)\n[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/LlamaIndex?style=plastic&logo=reddit&label=r%2FLlamaIndex&labelColor=white)](https://www.reddit.com/r/LlamaIndex/)\n[![Ask AI](https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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"},{"ref":"P5","kind":"page","title":"friendliai/locust_exporter repository metadata","date":"2026-06-11T04:19:24.985905+00:00","date_source":null,"source_url":"https://github.com/friendliai/locust_exporter","signal_url":null,"signal_json_url":null,"text":"# friendliai/locust_exporter\n\nDescription: A Locust metrics exporter for Prometheus\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-03-18T08:03:02Z\n\nPushed: 2024-03-18T09:31:20Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: ContainerSolutions/locust_exporter\n\nArchived: no\n\nREADME:\n# Locust Exporter\n\nPrometheus exporter for [Locust](https://github.com/locustio/locust). This exporter was inspired by [mbolek/locust_exporter](https://github.com/mbolek/locust_exporter).\n\n[![Docker Pulls](https://img.shields.io/docker/pulls/containersol/locust_exporter.svg)](https://hub.docker.com/r/containersol/locust_exporter/tags) [![license](https://img.shields.io/github/license/ContainerSolutions/locust_exporter.svg)](https://github.com/ContainerSolutions/locust_exporter/blob/master/LICENSE)\n\n![locust_dashboard](locust_dashboard.png)\n\n## Quick Start\n\nThis package is available for Docker:\n\n1. Run Locust ([example docker-compose](https://github.com/locustio/locust/blob/master/examples/docker-compose/docker-compose.yml))\n\n2. Run Locust Exporter\n\nwith docker:\n\n```bash\ndocker run --net=host containersol/locust_exporter\n```\n\nor with docker-compose:\n\n```yaml\nversion: \"3.0\"\n\nservices:\nlocust-exporter:\nimage: containersol/locust_exporter\nnetwork_mode: \"host\"\n```\n\n3. Modify `prometheus.yml` to add target to Prometheus\n\n```yaml\nscrape_configs:\n- job_name: 'locust'\nstatic_configs:\n- targets: ['<LOCUST_IP>:9646']\n```\n\n4. Add dashboard to Grafana with ID [11985](https://grafana.com/grafana/dashboards/11985)\n\n## Building and Running\n\nThe default way to build is:\n\n```bash\ngo get github.com/ContainerSolutions/locust_exporter\ncd ${GOPATH-$HOME/go}/src/github.com/ContainerSolutions/locust_exporter/\ngo run main.go\n```\n\n### Flags\n\n- `--locust.uri`\nAddress of Locust. Default is `http://localhost:8089`.\n\n- `--locust.timeout`\nTimeout request to Locust. Default is `5s`.\n\n- `--web.listen-address`\nAddress to listen on for web interface and telemetry. Default is `:9646`.\n\n- `--web.telemetry-path`\nPath under which to expose metrics. Default is `/metrics`.\n\n- `--locust.namespace`\nNamespace for prometheus metrics. Default `locust`.\n\n- `--log.level`\nSet logging level"},{"ref":"P6","kind":"page","title":"Senior Product Manager","date":"2026-06-11T04:12:53.967996+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/a6364382-e30a-4a30-919d-2c6b6c78185a","signal_url":null,"signal_json_url":null,"text":"# Senior Product Manager\n\nTeam: Product\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-03-15T13:07:46.886+00:00\n\nABOUT THE JOB\n\nFriendliAI is seeking a Senior Product Manager to drive the strategy and execution of our AI inference platform. This is an individual contributor role with significant product leadership responsibility, including guiding junior Product Managers and Product Designers across shared product areas.\n\nYou will own key product initiatives end-to-end while helping raise the overall bar for product thinking, execution, and decision-making within the product organization. This role sits at the intersection of customer needs, engineering reality, and business strategy, and plays a critical role in how FriendliAI’s platform evolves as we scale.\n\nKEY RESPONSIBILITIES\n\n- Own product strategy and roadmap for core areas of FriendliAI’s inference platform, including model APIs, deployment workflows, and developer-facing features.\n\n- Lead product initiatives end-to-end, from discovery and prioritization through delivery and iteration.\n\n- Guide and mentor junior Product Managers and Product Designers by setting direction, reviewing work, and providing structured feedback.\n\n- Drive customer discovery through interviews, design partnerships, usage data analysis, and direct engagement with enterprise and developer users.\n\n- Identify high-impact opportunities by analyzing market trends, competitive landscape, and emerging patterns in AI inference and infrastructure.\n\n- Translate customer and business needs into clear product requirements, user stories, and success metrics.\n\n- Partner closely with engineering and research teams to scope solutions, make tradeoffs, and deliver high-quality outcomes.\n\n- Collaborate with GTM, Sales, and Solutions teams to support customer conversations, roadmap alignment, and product positioning.\n\n- Define and track product success metrics and KPIs, using data and feedback to guide prioritization and iteration.\n\n- Clearly communicate product vision, priorities, and tradeoffs to stakeholders across the company.\n\nQUALIFICATIONS\n\n- 6+ years of product management experience, with demo"},{"ref":"P7","kind":"page","title":"Software Engineer – AI Inference Engine","date":"2026-06-11T04:12:53.959372+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/eebbe221-65e4-4061-a519-8571219b6b7b","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – AI Inference Engine\n\nTeam: Inference Systems\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-15T13:17:26.899+00:00\n\nABOUT THE JOB\n\nWe are seeking a highly technical Inference Engine Engineer to optimize the performance and efficiency of our core inference engine. In this role, you will focus on designing, implementing, and optimizing GPU kernels and supporting infrastructure for next-generation generative and agentic AI workloads. Your work will directly power the most latency-critical and compute-intensive systems deployed by our customers.\n\nWe are looking for an exceptional engineer with a strong foundation in GPU programming and compiler infrastructure. The ideal candidate enjoys pushing performance boundaries and has experience supporting production-scale machine learning applications.\n\nKEY RESPONSIBILITIES\n\n- Design and optimize custom GPU kernels for AI (e.g., transformer and diffusion) workloads\n\n- Contribute to the development of FriendliAI’s kernel compiler, memory planner, runtime, and other core components.\n\n- Collaborate with cloud and infrastructure engineers to ensure end-to-end inference performance\n\n- Analyze performance bottlenecks across the software and hardware stack, and implement targeted optimizations\n\n- Drive support for new model architectures and tensor compute patterns\n\n- Maintain production-grade performance infrastructure, including profiling, benchmarking, and validation tools\n\nQUALIFICATIONS\n\n- 5+ years of experience in production or high-impact research environments\n\n- Production-level expertise in Python and C++\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent\n\n- Experience developing machine learning frameworks or performance-critical runtime systems\n\n- Hands-on experience writing and optimizing GPU kernels\n\n- Hands-on experience profiling GPU kernels\n\n- Experience working with generative AI models such as transformer and diffusion models\n\nPREFERRED EXPERIENCE\n\n- Experience developing machine learning compilers or code generation systems\n\n- Familiarity with dynamic shape compilation,"},{"ref":"P8","kind":"page","title":"Software Engineer – GPU Kernel","date":"2026-06-11T04:12:53.704679+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/cb6e7c74-c22a-4192-aec8-65769839740b","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – GPU Kernel\n\nTeam: Inference Systems\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-15T13:19:53.768+00:00\n\nABOUT THE JOB\n\nFriendliAI is looking for a GPU Kernel Engineer to design, build, and optimize the low-level compute kernels that power our large-scale, GPU-accelerated AI inference platform. You will be delivering world-class inference speed across NVIDIA and AMD GPUs. With our recent $20M funding, we are scaling our team to meet market demand.\n\nThis is a deeply technical, high-impact role where you will write GPU code, implement advanced optimizations. As part of our engine team, you will contribute directly to the company’s proprietary inference engine which supports over 450,000 models on Hugging Face. You will work with the inventors of continuous batching and collaborate with the platform team to deploy your work into production.\n\nKEY RESPONSIBILITIES\n\n- Design, implement, and optimize high-performance GPU kernels for AI inference (e.g., GEMM, attention, routing)\n\n- Develop and maintain GPU code in CUDA and C++, including low-level assembly when needed\n\n- Implement reduced-precision and quantized kernels (FP8/FP4) for low-latency or high-throughput inference\n\n- Benchmark and ensure cross-vendor performance parity between NVIDIA and AMD hardware\n\n- Contribute to internal GPU libraries and tune performance of performance-critical components\n\n- Accelerate multi-modal model pipelines\n\n- Investigate and integrate next-generation GPU features\n\nQUALIFICATIONS\n\n- 3+ years of experience in GPU programming, HPC, or performance-critical systems\n\n- Bachelor’s or Master’s degrees in Computer Science, Computer Engineering, Electrical Engineering, or a related field\n\n- Strong proficiency in CUDA for NVIDIA GPUs or ROCm/HIP for AMD GPUs\n\n- Deep understanding of GPU architecture: warps, threads, memory hierarchy, synchronization, and latency-throughput trade-offs\n\n- Proficiency in C++\n\n- Experience with GPU profiling and performance tuning\n\n- Strong numerical background with understanding of precision trade-offs and quantization techniques\n\nPREFERRED EXPERIENCE\n\n- Experience optimizing t"},{"ref":"P9","kind":"page","title":"Solutions Architect - AI Inference Specialist","date":"2026-06-11T04:12:53.680053+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/f383e666-a99a-4574-b270-4e1738734706","signal_url":null,"signal_json_url":null,"text":"# Solutions Architect - AI Inference Specialist\n\nTeam: Solutions Architect\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-15T13:50:18.060+00:00\n\nAbout the job\n\nFriendliAI is seeking a Solution Architect to assist enterprises in deploying, scaling, and operating generative and agentic AI workloads on FriendliAI infrastructure. You will work directly with customers to solve and implement production-grade applications using our products, such as Serverless Endpoints, Dedicated Endpoints, or Container.\n\nFriendli Container is our service that allows customers to download our inference engine as Docker images and deploy it in their chosen environment, such as private clouds or on-premises. Our Friendli Container can be adopted directly to AWS EKS clusters using our EKS add-on product.\n\nYou will work directly on our customers’ projects, collaborating with their engineering teams to solve AI inference challenges like scaling, orchestration, and monitoring. This is a hands-on, customer-embedded role. If you have worked in DevOps, platform engineering, or SRE for AI applications, this is your ideal position.\n\nKey Responsibilities\n\n- Design and implement large-scale deployment architectures for LLM and multimodal inference\n\n- Deploy and manage containerized workloads across Kubernetes clusters\n\n- Diagnose production issues, such as performance bottlenecks, and implement temporary fixes as needed\n\n- Collaborate with customers’ DevOps teams to integrate FriendliAI’s infrastructure into their CI/CD workflows\n\n- Develop scripts, Helm charts, and Terraform modules that simplify repeated deployments\n\n- Contribute field insights to shape our platform reliability, observability, and scaling strategies\n\n- Lead workshops, technical sessions, or webinars to help customers master infrastructure best practices.\n\nQualifications\n\n- 3+ years of experience in cloud infrastructure, DevOps, or reliability engineering\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent\n\n- Proficiency with Kubernetes, Docker, Terraform, and Helm\n\n- Strong foundation in distributed systems"},{"ref":"P10","kind":"page","title":"Solutions Architect - AI Model Specialist","date":"2026-06-11T04:12:53.676078+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/b131cb30-8386-407e-a984-a75d5f2845da","signal_url":null,"signal_json_url":null,"text":"# Solutions Architect - AI Model Specialist\n\nTeam: Solutions Architect\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-15T13:51:18.989+00:00\n\nAbout the job\n\nFriendliAI is seeking a Solution Architect specializing in open-source AI models, AI inference API integration, and agentic systems. You will work closely with our customers to integrate FriendliAI’s inference and agent frameworks into real-world products, enabling them to build and scale AI applications effectively.\n\nYou will work directly on our customers’ projects, collaborating with their engineering teams to solve challenges in integrating tools, environments, and models with AI agents. This is a hands-on, customer-embedded role.\n\nKey Responsibilities\n\n- Design and implement AI-powered products using FriendliAI’s APIs\n\n- Guide customers on selecting, evaluating, and operating AI models across different domains\n\n- Integrate and extend open-source frameworks for FriendliAI integration\n\n- Build and deploy custom inference endpoints, chat flows, and multi-agent orchestration pipelines\n\n- Develop SDKs, example applications, and reference APIs for agentic and generative AI use cases\n\n- Provide deep technical guidance on prompt engineering, API composition, and workflow orchestration\n\n- Debug and optimize context and memory across long-running agent sessions\n\n- Gather customer feedback and translate it into product-level improvements\n\n- Lead technical demos, developer workshops, or webinars\n\nQualifications\n\n- 3+ years of software engineering experience, ideally in backend or API development\n\n- Proficient in Python and modern web frameworks (FastAPI, Flask, or similar)\n\n- Strong experience deploying LLMs and integrating into generative AI APIs\n\n- Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGen, etc.)\n\n- Strong experience in integrating open-source generative AI models into applications\n\n- Excellent communication skills and a passion for improving developer experience\n\n- Excellent problem-solving and debugging skills in real-world environments\n\nPreferred Experience\n\n- Contributions to open-source AI libraries or projects\n\n- Familiari"},{"ref":"P11","kind":"page","title":"Software Engineer – Senior Backend","date":"2026-06-11T04:12:53.673924+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/5fd918c3-70c8-44f1-b080-2aef9520b312","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – Senior Backend\n\nTeam: Core Product\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-03-15T13:31:27.577+00:00\n\nABOUT THE JOB\n\nWe believe using large language and multimodal models should be as simple as calling an API. To achieve this in production, we need to serve enterprises across clouds, with authentication, billing, multi-tenant isolation, and zero tolerance for downtime.\n\nWe are looking for a Senior Backend Engineer who is excited by the full breadth of what it takes to run a platform in production. You will own the business logic layer that sits between our inference engine and every customer who relies on it. Your work spans API engineering, service development, and data architecture. If you like solving problems that only reveal themselves in the wild, this is your role: edge cases in multi-cloud orchestration, enterprise requirements that don’t fit neatly into a spec, performance bottlenecks that are hard to reproduce.\n\nYou will move across domains, make decisions under uncertainty, and build systems that work cleanly, reliably, and at scale. We are looking for people with a track record of owning complex systems in production and solving unique problems. A great candidate is a strong collaborator who enjoys solving complex architectural challenges, cares deeply about developer workflows, and is eager to help define the future of AI adoption.\n\nKEY RESPONSIBILITIES\n\n- Own the architecture and evolution of core backend microservices powering our AI inference platform, from the API layer through business logic to the data layer.\n\n- Design and build production-grade APIs (REST, gRPC, GraphQL) that serve as the foundation for AI deployments, developer integrations, and enterprise workflows.\n\n- Build and scale enterprise-grade platform capabilities: authentication, RBAC, billing, organization management, and secure multi-tenant SaaS infrastructure.\n\n- Develop AI-specific platform features, including LLM deployment workflows and inference-specific service integrations.\n\n- Design and optimize data models and pipelines across OLTP (PostgreSQL) and OLAP (ClickHouse) systems.\n\n- Collaborate wit"},{"ref":"P12","kind":"page","title":"Software Engineer – AI Agents","date":"2026-06-11T04:12:53.668546+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/2c922001-cccf-4006-9730-5488d4efaba6","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – AI Agents\n\nTeam: Applied Engineering\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-03-24T14:35:58.676+00:00\n\nABOUT THE JOB\n\nWe’re seeking an Agent Engineer to design and build agentic features in our platform, including document understanding, advanced RAG, and customer support automation. In this role, you will develop not only the agent components themselves, but also the Friendli Agent API, which serves as the core developer interface for building and extending agent applications. You will also build agent applications as production-ready examples of how agents can solve real-world problems.\n\nThese applications will be primarily written in Python and will serve as reference implementations for our customers and community. We are looking for a hands-on engineer who is passionate about building agent systems and making AI easy for developers to adopt. The ideal candidate is comfortable creating agent applications that showcase what is possible, is curious about and experienced with open-source models, and enjoys turning them into reliable, high-impact features.\n\nKEY RESPONSIBILITIES\n\n- Design, build, and maintain agent APIs and applications that deliver document understanding and other high-value features\n\n- Evaluate and integrate open-source models to power production-ready agent features where possible\n\n- Develop reference agent applications to showcase workflows and accelerate customer adoption\n\n- Collaborate with backend and infrastructure teams to integrate agents with deployment, orchestration, and monitoring systems\n\n- Ensure APIs are robust, developer-friendly, and enterprise-ready through strong design principles and documentation\n\n- Continuously improve the reliability, scalability, and performance of agent features in production\n\nQUALIFICATIONS\n\n- 3+ years of experience in software engineering, preferably in backend, ML systems, or API development\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, or equivalent\n\n- Strong programming skills in Python; experience with various Python frameworks\n\n- Solid understanding of LLM workflows, agent patterns, or too"},{"ref":"P13","kind":"page","title":"QA Engineer","date":"2026-06-11T04:12:53.061566+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/41859c5f-fd60-40a3-9966-39a78852008b","signal_url":null,"signal_json_url":null,"text":"# QA Engineer\n\nTeam: Core Product\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-27T07:23:50.181+00:00\n\nABOUT THE JOB\n\nFriendli Suite is our SaaS platform that includes microservices, a frontend, multi-cloud infrastructure, enterprise authentication, billing, and organization management. However, what makes this role unique is that our platform delivers AI inference. Validating whether inference works well is a problem that traditional QA methods do not fully solve. A deployment can succeed technically and still produce poor inference.\n\nWe are looking for a dedicated QA engineer who can own the product's quality, ensuring our product works the way any well-run SaaS platform should, while also developing the approaches needed to validate AI inference quality, model deployments, and integrations that traditional testing alone cannot cover.\n\nKEY RESPONSIBILITIES\n\n- Own quality across FriendliAI's full platform stack: backend microservices, frontend, model deployments, and inference pipelines.\n\n- Build and maintain automated test suites using pytest, covering unit, integration, and regression testing across backend services.\n\n- Develop and run load and scalability tests using Locust to validate platform performance under real-world conditions.\n\n- Own frontend and end-to-end testing with Playwright across the full user-facing product.\n\n- Design and implement test strategies that account for LLM inference.\n\n- Work closely with infrastructure and backend engineers to validate model deployment workflows, multi-cloud orchestration, and service integrations.\n\n- Identify coverage gaps, prioritize test investment, and build tooling and pipelines.\n\nQUALIFICATIONS\n\n- 3+ years of experience in software quality engineering, with a track record of owning test strategy.\n\n- Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent.\n\n- Proficiency in Python and hands-on experience with pytest for test automation.\n\n- Experience with load and performance testing tools such as Locust.\n\n- Experience with browser automation and end-to-end testing frameworks such as Playwright.\n\n- Working knowledge"},{"ref":"P14","kind":"page","title":"Software Engineer – Platform Security","date":"2026-06-11T04:12:53.01696+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/372dbe1e-d6e6-4726-ae09-673ec8916d05","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – Platform Security\n\nTeam: Infrastructure\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-27T07:31:42.955+00:00\n\nAbout the job\n\nFriendliAI is seeking a Forward Deployed Engineer (FDE) to assist enterprises in deploying, scaling, and operating generative and agentic AI workloads on FriendliAI infrastructure. You will work directly with customers to solve and implement production-grade applications using our products, such as Serverless Endpoints, Dedicated Endpoints, or Container.\n\nFriendli Container is our service that allows customers to download our inference engine as Docker images and deploy it in their chosen environment, such as private clouds or on-premises. Our Friendli Container can be adopted directly to AWS EKS clusters using our EKS add-on product.\n\nYou will work directly on our customers’ projects, collaborating with their engineering teams to solve AI inference challenges like scaling, orchestration, and monitoring. This is a hands-on, customer-embedded role. If you have worked in DevOps, platform engineering, or SRE for AI applications, this is your ideal position.\n\nKey Responsibilities\n\n- Design and implement large-scale deployment architectures for LLM and multimodal inference\n\n- Deploy and manage containerized workloads across Kubernetes clusters\n\n- Diagnose production issues, such as performance bottlenecks, and implement temporary fixes as needed\n\n- Collaborate with customers’ DevOps teams to integrate FriendliAI’s infrastructure into their CI/CD workflows\n\n- Develop scripts, Helm charts, and Terraform modules that simplify repeated deployments\n\n- Contribute field insights to shape our platform reliability, observability, and scaling strategies\n\n- Lead workshops, technical sessions, or webinars to help customers master infrastructure best practices\n\nQualifications\n\n- 3+ years of experience in cloud infrastructure, DevOps, or reliability engineering\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent\n\n- Proficiency with Kubernetes, Docker, Terraform, and Helm\n\n- Strong foundation in distributed systems,"},{"ref":"P15","kind":"page","title":"Software Engineer - Full Stack","date":"2026-06-11T04:12:52.353172+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/25429014-12bd-4665-a435-367d71a785c2","signal_url":null,"signal_json_url":null,"text":"# Software Engineer - Full Stack\n\nTeam: Core Product\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-03-27T07:33:54.963+00:00\n\nABOUT THE JOB\n\nWe’re seeking a Full-Stack Engineer to design, build, and scale our web platform, which serves as the core interface for deploying multimodal models, observing workloads, and building agent workflows. In this role, you’ll work closely with product, infrastructure, and design teams to create high-performance, developer-friendly, and enterprise-ready tools.\n\nWe are looking for a hands-on engineer who is eager to work at the intersection of infrastructure, developer experience, and AI applications. The ideal candidate is a talented full-stack developer, strong collaborator, and someone who enjoys working across the stack, cares deeply about developer workflows, and is excited to help define the future of AI adoption.\n\nKEY RESPONSIBILITIES\n\n- Design, build, and maintain web applications and tools for AI model deployment, monitoring, and performance optimization\n\n- Develop clean, scalable, and robust APIs powering AI agents, workflows, and user-facing systems\n\n- Collaborate with infrastructure engineers to integrate backend systems with deployment and orchestration pipelines\n\n- Optimize the performance and usability of web interfaces\n\n- Drive code quality through automated testing, CI/CD, and code reviews\n\n- Contribute to architecture and design decisions that shape our platform’s long-term direction\n\n- Identify and resolve technical debt and improve system reliability in production systems\n\nQUALIFICATIONS\n\n- 5+ years of industry experience in full-stack or backend engineering\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, or equivalent\n\n- Fluent in TypeScript and Python, Expert with React/Next.js\n\n- Strong backend experience with FastAPI or similar Python frameworks\n\n- Proven expertise in delivering production-scale full-stack applications\n\n- Proficiency in designing data models, writing SQL, and working with PostgreSQL\n\n- Deep understanding of modern web frameworks and component-driven architecture\n\n- Strong API design experience across gRP"},{"ref":"P16","kind":"page","title":"Software Engineer – AI Agents","date":"2026-06-11T04:12:52.331686+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/6b7dbaf7-8751-402e-b253-ad968f7dc362","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – AI Agents\n\nTeam: Applied Engineering\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-04-15T17:37:36.461+00:00\n\nABOUT THE JOB\n\nWe’re seeking an Agent Engineer to design and build agentic features in our platform, including document understanding, advanced RAG, and customer support automation. In this role, you will develop not only the agent components themselves, but also the Friendli Agent API, which serves as the core developer interface for building and extending agent applications. You will also build agent applications as production-ready examples of how agents can solve real-world problems.\n\nThese applications will be primarily written in Python and will serve as reference implementations for our customers and community. We are looking for a hands-on engineer who is passionate about building agent systems and making AI easy for developers to adopt. The ideal candidate is comfortable creating agent applications that showcase what is possible, is curious about and experienced with open-source models, and enjoys turning them into reliable, high-impact features.\n\nKEY RESPONSIBILITIES\n\n- Design, build, and maintain agent APIs and applications that deliver document understanding and other high-value features\n\n- Evaluate and integrate open-source models to power production-ready agent features where possible\n\n- Develop reference agent applications to showcase workflows and accelerate customer adoption\n\n- Collaborate with backend and infrastructure teams to integrate agents with deployment, orchestration, and monitoring systems\n\n- Ensure APIs are robust, developer-friendly, and enterprise-ready through strong design principles and documentation\n\n- Continuously improve the reliability, scalability, and performance of agent features in production\n\nQUALIFICATIONS\n\n- 3+ years of experience in software engineering, preferably in backend, ML systems, or API development\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, or equivalent\n\n- Strong programming skills in Python; experience with various Python frameworks\n\n- Solid understanding of LLM workflows, agent pattern"},{"ref":"P17","kind":"page","title":"Software Engineer – Python Developer Tools","date":"2026-06-11T04:12:52.27538+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/4cfe5d9f-d34f-4100-98b2-267c10238737","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – Python Developer Tools\n\nTeam: Applied Engineering\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-04-15T17:47:21.407+00:00\n\nABOUT THE JOB\n\nFriendliAI is hiring a Python Engineer to build developer tools for our users and internal engineers. The software developed by the engineer will be the primary way developers integrate with our inference and agent platform. Your work will remove friction for internal teams and external users. You’ll design ergonomic & stable APIs, reliable releases to PyPI, and drive top-tier developer experience across documentation, examples, and tooling. You’ll collaborate with frontend engineers on end-to-end workflows and partner with product & engineering teams.\n\nKEY RESPONSIBILITIES\n\n- Own the Python SDK lifecycle. You will design APIs and implement the client side in the SDK.\n\n- Build and maintain a cross-platform CLI with a modern interface, including helpful error messages and good UX.\n\n- Manage packaging and distribution pipelines.\n\n- Develop and maintain internal developer tools related to DevOps.\n\n- Create examples, templates, and guides that help developers effectively utilize our software bundles.\n\nQUALIFICATIONS\n\n- 3+ years of professional Python engineering building libraries, SDKs, or developer tools used in production.\n\n- Demonstrated SDK/CLI ownership. Familiarity with API ergonomics and versioning, deprecation policies, telemetry, and debugging customer issues.\n\n- Strong Python fundamentals, including asyncio, typing, packaging, and testing.\n\n- Web API fluency in REST and gRPC.\n\n- Experience working with Python monorepos.\n\n- Clear written communication skills and capable of turning complex features into clean APIs and concise docs.\n\nPREFERRED EXPERIENCE\n\n- Maintainer or significant OSS contributor in Python libraries (tooling, SDK, CLI, etc.).\n\n- Experience with Python AST and Meta-programming, Packaging\n\n- Performance work (profiling, streaming, backpressure) or multi-platform build experience.\n\n- Cross‑language exposure (TypeScript/Node, Go).\n\n- Containers & local dev tooling (Docker); familiarity with Kubernetes basics.\n\n- Experience with "},{"ref":"P18","kind":"page","title":"Software Engineer - Senior Backend ","date":"2026-06-11T04:12:52.170665+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/d5288f06-df73-407d-9d32-3eb96d735a51","signal_url":null,"signal_json_url":null,"text":"# Software Engineer - Senior Backend \n\nTeam: Core Product\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-04-15T17:57:22.176+00:00\n\nABOUT THE JOB\n\nWe believe using large language and multimodal models should be as simple as calling an API. To achieve this in production, we need to serve enterprises across clouds, with authentication, billing, multi-tenant isolation, and zero tolerance for downtime.\n\nWe are looking for a Senior Backend Engineer who is excited by the full breadth of what it takes to run a platform in production. You will own the business logic layer that sits between our inference engine and every customer who relies on it. Your work spans API engineering, service development, and data architecture. If you like solving problems that only reveal themselves in the wild, this is your role: edge cases in multi-cloud orchestration, enterprise requirements that don’t fit neatly into a spec, performance bottlenecks that are hard to reproduce.\n\nYou will move across domains, make decisions under uncertainty, and build systems that work cleanly, reliably, and at scale. We are looking for people with a track record of owning complex systems in production and solving unique problems. A great candidate is a strong collaborator who enjoys solving complex architectural challenges, cares deeply about developer workflows, and is eager to help define the future of AI adoption.\n\nKEY RESPONSIBILITIES\n\n- Own the architecture and evolution of core backend microservices powering our AI inference platform, from the API layer through business logic to the data layer.\n\n- Design and build production-grade APIs (REST, gRPC, GraphQL) that serve as the foundation for AI deployments, developer integrations, and enterprise workflows.\n\n- Build and scale enterprise-grade platform capabilities: authentication, RBAC, billing, organization management, and secure multi-tenant SaaS infrastructure.\n\n- Develop AI-specific platform features, including LLM deployment workflows and inference-specific service integrations.\n\n- Design and optimize data models and pipelines across OLTP (PostgreSQL) and OLAP (ClickHouse) systems.\n\n- Colla"},{"ref":"P19","kind":"page","title":"Customer Success Engineer (contract based)","date":"2026-06-11T04:12:52.152917+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/11f5ea91-9775-48a8-aca6-0438de737fd7","signal_url":null,"signal_json_url":null,"text":"# Customer Success Engineer (contract based)\n\nTeam: Applied Engineering\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-04-17T08:53:46.757+00:00\n\nABOUT THE JOB\n\nFriendliAI is seeking a Customer Success Engineer to act as the technical liaison between our customers and the engineering team behind our large-scale AI inference platform. You’ll help developers and enterprises deploy, scale, and optimize generative and agentic AI workloads running on FriendliAI infrastructure.\n\nIn this role, you will act as a trusted technical advisor to our users, ensuring smooth onboarding, ongoing support, and optimal product usage. You will collaborate across product, engineering, and support teams to represent the customer's voice and help shape a delightful developer experience.\n\n[Note] This is a 6-month contract position, and contract extension may be discussed based on performance during the employment period.\n\n \n\nKEY RESPONSIBILITIES\n\n- Guide new users through onboarding, setup, and best practices\n\n- Document technical learnings, common patterns, and solutions derived from real customer interactions\n\n- Transform lessons learned into precise, actionable knowledge for both internal teams and external users\n\n- Create and maintain documentation, tutorials, and sample projects\n\n- Provide technical support via Instant messages, Slack, email, and meetings, helping users troubleshoot and resolve issues quickly\n\n- Collaborate with engineering to debug production issues and prioritize fixes\n\n- Lead customer-facing technical sessions, demos, and Q&As\n\nQUALIFICATIONS\n\n- 2+ years in a developer-facing or technical support role (Customer Success, Solutions Engineering, etc.)\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent\n\n- Strong technical background in backend systems or AI tooling\n\n- Excellent written and verbal communication skills (both Korean and English)\n\n- Proficient in Python and familiar with LLM ecosystems (e.g., Hugging Face, LangChain)\n\n- Experience working with APIs, CLI tools, and deployment workflows\n\n- Ability to explain complex technical concepts clearly an"},{"ref":"P20","kind":"page","title":"Software Engineer – AI Inference Engine","date":"2026-06-11T04:12:52.085957+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/8da9914d-efe1-4bb1-96a9-a10eceab67fe","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – AI Inference Engine\n\nTeam: Inference Systems\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-04-22T01:29:38.565+00:00\n\nABOUT THE JOB\n\nWe are seeking a highly technical Inference Engine Engineer to optimize the performance and efficiency of our core inference engine. In this role, you will focus on designing, implementing, and optimizing GPU kernels and supporting infrastructure for next-generation generative and agentic AI workloads. Your work will directly power the most latency-critical and compute-intensive systems deployed by our customers.\n\nWe are looking for an exceptional engineer with a strong foundation in GPU programming and compiler infrastructure. The ideal candidate enjoys pushing performance boundaries and has experience supporting production-scale machine learning applications.\n\nKEY RESPONSIBILITIES\n\n- Design and optimize custom GPU kernels for AI (e.g., transformer and diffusion) workloads\n\n- Contribute to the development of FriendliAI’s kernel compiler, memory planner, runtime, and other core components.\n\n- Collaborate with cloud and infrastructure engineers to ensure end-to-end inference performance\n\n- Analyze performance bottlenecks across the software and hardware stack, and implement targeted optimizations\n\n- Drive support for new model architectures and tensor compute patterns\n\n- Maintain production-grade performance infrastructure, including profiling, benchmarking, and validation tools\n\nQUALIFICATIONS\n\n- 5+ years of experience in production or high-impact research environments\n\n- Production-level expertise in Python and C++\n\n- Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent\n\n- Experience developing machine learning frameworks or performance-critical runtime systems\n\n- Hands-on experience writing and optimizing GPU kernels\n\n- Hands-on experience profiling GPU kernels\n\n- Experience working with generative AI models such as transformer and diffusion models\n\nPREFERRED EXPERIENCE\n\n- Experience developing machine learning compilers or code generation systems\n\n- Familiarity with dynamic shape compilation, memory p"},{"ref":"P21","kind":"page","title":"Software Engineer – GPU Kernel","date":"2026-06-11T04:12:52.020569+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/cd4dd23b-cf94-46ec-afc3-f84037dca735","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – GPU Kernel\n\nTeam: Inference Systems\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-04-22T02:51:29.860+00:00\n\nABOUT THE JOB\n\nFriendliAI is looking for a GPU Kernel Engineer to design, build, and optimize the low-level compute kernels that power our large-scale, GPU-accelerated AI inference platform. You will be delivering world-class inference speed across NVIDIA and AMD GPUs. With our recent $20M funding, we are scaling our team to meet market demand.\n\nThis is a deeply technical, high-impact role where you will write GPU code, implement advanced optimizations. As part of our engine team, you will contribute directly to the company’s proprietary inference engine which supports over 450,000 models on Hugging Face. You will work with the inventors of continuous batching and collaborate with the platform team to deploy your work into production.\n\nKEY RESPONSIBILITIES\n\n- Design, implement, and optimize high-performance GPU kernels for AI inference (e.g., GEMM, attention, routing)\n\n- Develop and maintain GPU code in CUDA and C++, including low-level assembly when needed\n\n- Implement reduced-precision and quantized kernels (FP8/FP4) for low-latency or high-throughput inference\n\n- Benchmark and ensure cross-vendor performance parity between NVIDIA and AMD hardware\n\n- Contribute to internal GPU libraries and tune performance of performance-critical components\n\n- Accelerate multi-modal model pipelines\n\n- Investigate and integrate next-generation GPU features\n\nQUALIFICATIONS\n\n- 3+ years of experience in GPU programming, HPC, or performance-critical systems\n\n- Bachelor’s or Master’s degrees in Computer Science, Computer Engineering, Electrical Engineering, or a related field\n\n- Strong proficiency in CUDA for NVIDIA GPUs or ROCm/HIP for AMD GPUs\n\n- Deep understanding of GPU architecture: warps, threads, memory hierarchy, synchronization, and latency-throughput trade-offs\n\n- Proficiency in C++\n\n- Experience with GPU profiling and performance tuning\n\n- Strong numerical background with understanding of precision trade-offs and quantization techniques\n\nPREFERRED EXPERIENCE\n\n- Experience optimizing transforme"},{"ref":"P22","kind":"page","title":"Developer Advocate ","date":"2026-06-11T04:12:51.983208+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/9df44597-ee03-4504-8bed-a842b6135b09","signal_url":null,"signal_json_url":null,"text":"# Developer Advocate \n\nTeam: Marketing\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-04-24T08:11:24.498+00:00\n\nRole Overview\n\nWe are seeking a Developer Advocate to build and lead our developer community engagement strategy. This role will serve as the bridge between FriendliAI and our developer community, creating meaningful connections through events, content, and technical advocacy.\n\nKey Responsibilities\n\n- Design and execute a comprehensive developer relations strategy aligned with company goals\n\n- Lead organization of developer-focused events including hackathons, meetups, and workshops\n\n- Create and oversee production of high-quality technical content for our blog and documentation\n\n- Develop engaging social media content highlighting our technology and community\n\n- Build relationships with key developer communities and technology influencers\n\n- Gather developer feedback to inform product roadmap and improvements\n\n- Represent FriendliAI at industry conferences and developer events\n\n- Collaborate with marketing, product, and engineering teams to ensure cohesive messaging\n\nRequirements\n\n- 5+ years of experience in developer relations, developer advocacy, or technical community management\n\n- Demonstrated success organizing technical events such as hackathons and developer meetups\n\n- Strong technical writing skills with a portfolio of technical blog posts or documentation\n\n- Experience creating developer-focused content for social media platforms\n\n- Technical background with hands-on programming experience\n\n- Excellent presentation and communication skills\n\n- Ability to explain complex technical concepts clearly to various audiences\n\n- Experience with GenAI technologies preferred\n\nBenefits\n\n- Flexible working hours\n\n- Daily lunch and dinner provided; unlimited snacks and beverages\n\n- Supportive and highly collaborative work environment\n\n- Health check-up support and top-tier equipment/hardware support\n\n- A front-row seat to the generative AI infrastructure revolution\n\n- Competitive compensation, startup equity, health insurance, and other benefits.\n\nAbout FriendliAI\n\nFriendliAI is building the world’s best AI "},{"ref":"P23","kind":"page","title":"Software Engineer – Python Developer Tools","date":"2026-06-11T04:12:51.904706+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/24a3b46f-6afd-4410-91ec-b51ef0ee8317","signal_url":null,"signal_json_url":null,"text":"# Software Engineer – Python Developer Tools\n\nTeam: Applied Engineering\n\nLocation: Seoul\n\nEmployment type: FullTime\n\nWorkplace type: OnSite\n\nRemote: no\n\nPublished: 2026-04-29T05:46:13.520+00:00\n\nABOUT THE JOB\n\nFriendliAI is hiring a Python Engineer to build developer tools for our users and internal engineers. The software developed by the engineer will be the primary way developers integrate with our inference and agent platform. Your work will remove friction for internal teams and external users. You’ll design ergonomic & stable APIs, reliable releases to PyPI, and drive top-tier developer experience across documentation, examples, and tooling. You’ll collaborate with frontend engineers on end-to-end workflows and partner with product & engineering teams.\n\nKEY RESPONSIBILITIES\n\n- Own the Python SDK lifecycle. You will design APIs and implement the client side in the SDK.\n\n- Build and maintain a cross-platform CLI with a modern interface, including helpful error messages and good UX.\n\n- Manage packaging and distribution pipelines.\n\n- Develop and maintain internal developer tools related to DevOps.\n\n- Create examples, templates, and guides that help developers effectively utilize our software bundles.\n\nQUALIFICATIONS\n\n- 3+ years of professional Python engineering building libraries, SDKs, or developer tools used in production.\n\n- Demonstrated SDK/CLI ownership. Familiarity with API ergonomics and versioning, deprecation policies, telemetry, and debugging customer issues.\n\n- Strong Python fundamentals, including asyncio, typing, packaging, and testing.\n\n- Web API fluency in REST and gRPC.\n\n- Experience working with Python monorepos.\n\n- Clear written communication skills and capable of turning complex features into clean APIs and concise docs.\n\nPREFERRED EXPERIENCE\n\n- Maintainer or significant OSS contributor in Python libraries (tooling, SDK, CLI, etc.).\n\n- Experience with Python AST and Meta-programming, Packaging\n\n- Performance work (profiling, streaming, backpressure) or multi-platform build experience.\n\n- Cross‑language exposure (TypeScript/Node, Go).\n\n- Containers & local dev tooling (Docker); familiarity with Kubernetes basics.\n\n- Experience with LLM/agent"},{"ref":"P24","kind":"page","title":"Developer Advocate ","date":"2026-06-11T04:12:51.786475+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/friendliai/8d4ac792-7b6b-4503-84a0-a321ee68fe71","signal_url":null,"signal_json_url":null,"text":"# Developer Advocate \n\nTeam: Marketing\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-05-13T15:36:58.434+00:00\n\nRole Overview\n\nWe are seeking a Developer Advocate to build and lead our developer community engagement strategy. This role will serve as the bridge between FriendliAI and the broader AI developer ecosystem, creating meaningful connections through technical content, community engagement, events, and developer advocacy.\n\nYou will play a key role in growing FriendliAI's presence within the GenAI, inference, and ML infrastructure communities by engaging directly with developers, AI engineers, and technical decision makers across both online and in-person channels.\n\nKey Responsibilities\n\n- Design and execute a comprehensive developer relations strategy aligned with FriendliAI's company goals, developer adoption initiatives, and community growth\n\n- Lead organization and execution of hackathons, technical meetups, workshops, webinars, and conference activations that engage AI engineers, ML practitioners, and infrastructure developers\n\n- Create and oversee high-quality technical content, including blog posts, inference benchmarks, architecture deep dives, integration tutorials, videos, demos, and developer documentation\n\n- Develop engaging social and community content that highlights FriendliAI's technology, product launches, open-source initiatives, and community activities\n\n- Build and maintain relationships with developer communities, open-source contributors, technical influencers, and strategic ecosystem partners\n\n- Gather developer feedback to inform product roadmap and improvements\n\n- Represent FriendliAI at industry conferences and developer events\n\n- Collaborate with marketing, product, and engineering teams to ensure cohesive messaging\n\nRequirements\n\n- 5+ years of experience in developer relations, developer advocacy, or technical community management\n\n- Demonstrated success organizing technical events such as hackathons and developer meetups\n\n- Strong technical writing skills with a portfolio of technical blog posts or documentation\n\n- Experience creating developer-focused content for socia"},{"ref":"P25","kind":"page","title":"friendliai/periflow-cli repository metadata","date":"2026-06-11T04:09:59.81055+00:00","date_source":null,"source_url":"https://github.com/friendliai/periflow-cli","signal_url":null,"signal_json_url":null,"text":"# friendliai/periflow-cli\n\nDescription: Welcome to PeriFlow CLI ☁︎\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 12\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2021-12-16T07:33:16Z\n\nPushed: 2023-08-03T04:41:40Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# ⛔️ IMPORTANT\n\nThis repository has been deprecated and is no longer maintained. Please use **[periflow-client](https://github.com/friendliai/periflow-client)** to continue receiving updates and support. You can install the package with `pip install periflow-client`.\n\n# PeriFlow CLI\n\n<p align=\"center\">\n<img src=\"./doc/assets/logo.svg\" width=\"30%\" alt=\"system\">\n</p>\n\nWelcome to PeriFlow ☁︎\n\nPeriFlow is a reliable, speedy, and efficient service for training and serving your own large-scale AI model on any data of your choice. PeriFlow makes use of the cloud infrastructure without your need to invest in on-premise supercomputers. With one click, PeriFlow loads your data and runs optimized massive-scale AI training, handling any headaches that may arise plus visualizing your training progress.\n\nPlease visit [docs.periflow.ai](https://docs.periflow.ai) for the detailed guide:\n\n- [CLI documentation](https://docs.periflow.ai/cli/intro)\n- [Python SDK documentation](https://docs.periflow.ai/sdk/intro)\n- [PeriFlow tutorials](https://docs.periflow.ai/tutorial/intro)\n\n## Installation\n\nYou can simply install the package using `pip`. Python version >= 3.8 is required.\n\n```sh\npip install periflow-cli\n```\n\n## Basic Commands\n\nPeriFlow CLI commands start with the app name prefix `pf`.\n\n```sh\npf [OPTIONS] COMMAND [ARGS]...\n```\n\nYou can see the detail of each command using one of the following:\n\n```sh\npf COMMAND\npf COMMAND -h\npf COMMAND --help\n```\n\n## Workflow\n\nYou may go through the following steps when training AI models in PeriFlow.\n\n1. Sign in.\n2. Create/upload a dataset.\n3. Run a job and monitor the job status.\n4. Download trained model checkpoints to your local computer or deploy it!\n\n### Step 1. Sign In\n\nYou can login to PeriFlow with the following command.\n\n```sh\npf login\n```\n\n### Step 2. Manage Datasets\n\nPeriFlow manages multiple datasets for your jobs. Once you create a dataset, the dataset can be easily used in any "},{"ref":"P26","kind":"page","title":"friendliai/FAI-Model repository metadata","date":"2026-06-11T04:09:58.400149+00:00","date_source":null,"source_url":"https://github.com/friendliai/FAI-Model","signal_url":null,"signal_json_url":null,"text":"# friendliai/FAI-Model\n\nDescription: FriendliAI Model Hub\n\nLanguage: Python\n\nStars: 90\n\nForks: 2\n\nOpen issues: 0\n\nCreated: 2022-05-19T08:07:53Z\n\nPushed: 2022-06-09T06:20:08Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# GPT-FAI 13B\nThis repository introduces our GPT-FAI 13B model. GPT-FAI 13B is a GPT-3 model with 13 billion parameters, which is a large autoregressive language model pretrained by [FriendliAI](https://friendli.ai/).\nGPT-FAI 13B is pretrained using [PeriFlow](https://friendli.ai/product), our service for training and serving large-scale AI models.\nWith PeriFlow, anyone can train large-scale models on hundreds of GPUs or more in the cloud easily. PeriFlow training is fast, thanks to our training optimization technologies and quickly and automatically handles various faults that may arise during training.\n\nYou can train and serve transformer models such as GPT-FAI 13B or bigger ones on PeriFlow. PeriFlow provides an optimized serving system that shows an order of magnitude higher performance than current state-of-the-art systems like NVIDIA [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). If you are interested in PeriFlow, please contact us at (sales@friendli.ai).\n\nRefer to the following sections for more details.\n* [Pretrained Model](https://github.com/friendliai/FAI-Model/blob/main/README.md#pretrained-model)\n* [Model Performance](https://github.com/friendliai/FAI-Model/blob/main/README.md#performance)\n* [PeriFlow](https://github.com/friendliai/FAI-Model/blob/main/README.md#periflow)\n* [Usage](https://github.com/friendliai/FAI-Model/blob/main/README.md#usage)\n* [Ethics](https://github.com/friendliai/FAI-Model/blob/main/README.md#ethics)\n* [Discussion](https://github.com/friendliai/FAI-Model/blob/main/README.md#discussion)\n* [Citation](https://github.com/friendliai/FAI-Model/blob/main/README.md#citation)\n\n## Pretrained Model\nYou can download the fp16 weights of GPT-FAI 13B from the following [link](https://forms.gle/PBqpHWAtZHXdYV9L6).\nGPT-FAI 13B can be used solely for your non-commercial research purposes.\n\n## Performance\nWe evaluated our model on various downstream tasks with [lm-evaluation-harness](https://github.com"},{"ref":"P27","kind":"page","title":"friendliai/llm-hackathon-tutorial repository metadata","date":"2026-06-11T04:09:57.959288+00:00","date_source":null,"source_url":"https://github.com/friendliai/llm-hackathon-tutorial","signal_url":null,"signal_json_url":null,"text":"# friendliai/llm-hackathon-tutorial\n\nDescription: FriendliAI LLM Hackathon tutorial scripts\n\nLanguage: Jupyter Notebook\n\nStars: 6\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2024-05-21T05:41:19Z\n\nPushed: 2024-12-02T00:52:50Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n<p align=\"center\">\n<img width=\"10%\" alt=\"Friendli Logo\" src=\"https://friendli.ai/icon.svg\">\n</p>\n\n<h2><p align=\"center\">LLM Hackathon with Friendli 🚀</p></h2>\n\nWelcome to Friendli Suite, the ultimate solution for serving generative AI models. We offer several guides to use full power of Friendli Suite and LLMs.\n\n# LLM Guides for Friendli Hackathon Users\n\n## Build Chat UI with Gradio\n\n[01-build-chat-ui-with-gradio.ipynb](01-build-chat-ui-with-gradio.ipynb)\n\n## How to prepare dataset for Fine-tuning\n\n[05-finetuning-dataset-preparation.ipynb](05-finetuning-dataset-preparation.ipynb)\n\n## Building a RAG Chatbot with Friendli, MongoDB Atlas, and LangChain\n\n[06-a-rag-with-langchain.ipynb](06-a-rag-with-langchain.ipynb)\n\n## Building a RAG Chatbot with Friendli Suite Documents and Endpoints\n\n[06-b-rag-with-friendli-suite.ipynb](06-b-rag-with-friendli-suite.ipynb)\n\n## Function Calling with Chat API\n\n[07-function-calling.ipynb](07-function-calling.ipynb)"},{"ref":"P28","kind":"page","title":"friendliai/friendli-client repository metadata","date":"2026-06-11T04:09:57.957533+00:00","date_source":null,"source_url":"https://github.com/friendliai/friendli-client","signal_url":null,"signal_json_url":null,"text":"# friendliai/friendli-client\n\nDescription: [⛔️ DEPRECATED] Friendli: the fastest serving engine for generative AI\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 50\n\nForks: 7\n\nOpen issues: 3\n\nCreated: 2023-07-20T12:57:24Z\n\nPushed: 2025-06-25T05:46:33Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n<!---\nCopyright (c) 2022-present, FriendliAI Inc. All rights reserved.\n-->\n\n# DEPRECATED [![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)\n\nThis is no longer supported, please consider using [Friendli Python SDK](https://pypi.org/project/friendli/) instead.\n\n---\n\n<p align=\"center\">\n<img width=\"10%\" alt=\"Friendli Logo\" src=\"https://friendli.ai/icon.svg\">\n</p>\n\n<h2><p align=\"center\">Supercharge Generative AI Serving with Friendli 🚀</p></h2>\n\n<p align=\"center\">\n<a href=\"https://github.com/friendliai/friendli-client/actions/workflows/ci.yaml\">\n<img alt=\"CI Status\" src=\"https://github.com/friendliai/friendli-client/actions/workflows/ci.yaml/badge.svg\">\n</a>\n<a href=\"https://pypi.org/project/friendli-client/\">\n<img alt=\"Python Version\" src=\"https://img.shields.io/pypi/pyversions/friendli-client?logo=Python&logoColor=white\">\n</a>\n<a href=\"https://pypi.org/project/friendli-client/\">\n<img alt=\"PyPi Package Version\" src=\"https://img.shields.io/pypi/v/friendli-client?logo=PyPI&logoColor=white\">\n</a>\n<a href=\"https://friendli.ai/docs/\">\n<img alt=\"Documentation\" src=\"https://img.shields.io/badge/read-doc-blue?logo=ReadMe&logoColor=white\">\n</a>\n<a href=\"https://github.com/friendliai/friendli-client/blob/main/LICENSE\">\n<img alt=\"License\" src=\"https://img.shields.io/badge/License-Apache%202.0-green.svg?logo=Apache\">\n</a>\n</p>\n\nThe Friendli Client offers convenient interface to interact with endpoint services provided by [Friendli Suite](https://suite.friendli.ai/), the ultimate solution for serving generative AI models. Designed for flexibility and performance, it supports both synchronous and asynchronous operations, making it easy to integrate powerful AI capabilities into your applications.\n\n# Installation\n\nTo get started with Friendli, install the client package using `pip`:\n\n```sh\npip install friendli-client\n```\n\n> [!IMPORTANT]\n>"},{"ref":"E1","kind":"event","title":"Account Executive","date":"2026-06-10T20:55:54.847+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/e5c4337b-338d-43e6-9bda-b82178acc3d1","signal_url":"https://onlylabs.fyi/signals/e9e6fc29-2685-42e5-afab-0c7735c603c1","signal_json_url":"https://onlylabs.fyi/signals/e9e6fc29-2685-42e5-afab-0c7735c603c1/signal.json","text":"job_opened · Account Executive · signal_desk=hiring · occurred_at=2026-06-10T20:55:54.847+00:00 · url=https://jobs.ashbyhq.com/friendliai/e5c4337b-338d-43e6-9bda-b82178acc3d1 · raw={\"location\":\"San Francisco\",\"team\":\"Sales\",\"ats\":\"ashby\"}"},{"ref":"E2","kind":"event","title":"Developer Advocate ","date":"2026-05-13T15:36:58.434+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/8d4ac792-7b6b-4503-84a0-a321ee68fe71","signal_url":"https://onlylabs.fyi/signals/2d915114-6da5-41c9-bff8-c3a24625cff4","signal_json_url":"https://onlylabs.fyi/signals/2d915114-6da5-41c9-bff8-c3a24625cff4/signal.json","text":"job_opened · Developer Advocate  · signal_desk=hiring · occurred_at=2026-05-13T15:36:58.434+00:00 · url=https://jobs.ashbyhq.com/friendliai/8d4ac792-7b6b-4503-84a0-a321ee68fe71 · raw={\"location\":\"San Francisco\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E3","kind":"event","title":"Software Engineer – Python Developer Tools","date":"2026-04-29T05:46:13.52+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/24a3b46f-6afd-4410-91ec-b51ef0ee8317","signal_url":"https://onlylabs.fyi/signals/9f2f4219-c64f-48b4-8acd-3c07a330b610","signal_json_url":"https://onlylabs.fyi/signals/9f2f4219-c64f-48b4-8acd-3c07a330b610/signal.json","text":"job_opened · Software Engineer – Python Developer Tools · signal_desk=hiring · occurred_at=2026-04-29T05:46:13.52+00:00 · url=https://jobs.ashbyhq.com/friendliai/24a3b46f-6afd-4410-91ec-b51ef0ee8317 · raw={\"location\":\"Seoul\",\"team\":\"Applied Engineering\",\"ats\":\"ashby\"}"},{"ref":"E4","kind":"event","title":"Developer Advocate ","date":"2026-04-24T08:11:24.498+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/9df44597-ee03-4504-8bed-a842b6135b09","signal_url":"https://onlylabs.fyi/signals/07534905-b62d-4e23-9d42-5a2d13d5e997","signal_json_url":"https://onlylabs.fyi/signals/07534905-b62d-4e23-9d42-5a2d13d5e997/signal.json","text":"job_opened · Developer Advocate  · signal_desk=hiring · occurred_at=2026-04-24T08:11:24.498+00:00 · url=https://jobs.ashbyhq.com/friendliai/9df44597-ee03-4504-8bed-a842b6135b09 · raw={\"location\":\"Seoul\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E5","kind":"event","title":"Software Engineer – GPU Kernel","date":"2026-04-22T02:51:29.86+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/cd4dd23b-cf94-46ec-afc3-f84037dca735","signal_url":"https://onlylabs.fyi/signals/5a7c0cf3-2090-4b41-9685-bee46001257e","signal_json_url":"https://onlylabs.fyi/signals/5a7c0cf3-2090-4b41-9685-bee46001257e/signal.json","text":"job_opened · Software Engineer – GPU Kernel · signal_desk=hiring · occurred_at=2026-04-22T02:51:29.86+00:00 · url=https://jobs.ashbyhq.com/friendliai/cd4dd23b-cf94-46ec-afc3-f84037dca735 · raw={\"location\":\"Seoul\",\"team\":\"Inference Systems\",\"ats\":\"ashby\"}"},{"ref":"E6","kind":"event","title":"Software Engineer – AI Inference Engine","date":"2026-04-22T01:29:38.565+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/8da9914d-efe1-4bb1-96a9-a10eceab67fe","signal_url":"https://onlylabs.fyi/signals/8cc54c03-63fe-49ef-ba5a-239c426ef342","signal_json_url":"https://onlylabs.fyi/signals/8cc54c03-63fe-49ef-ba5a-239c426ef342/signal.json","text":"job_opened · Software Engineer – AI Inference Engine · signal_desk=hiring · occurred_at=2026-04-22T01:29:38.565+00:00 · url=https://jobs.ashbyhq.com/friendliai/8da9914d-efe1-4bb1-96a9-a10eceab67fe · raw={\"location\":\"Seoul\",\"team\":\"Inference Systems\",\"ats\":\"ashby\"}"},{"ref":"E7","kind":"event","title":"Customer Success Engineer (contract based)","date":"2026-04-17T08:53:46.757+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/11f5ea91-9775-48a8-aca6-0438de737fd7","signal_url":"https://onlylabs.fyi/signals/c882c95f-e4f9-4de6-bd15-a3aff2aa039c","signal_json_url":"https://onlylabs.fyi/signals/c882c95f-e4f9-4de6-bd15-a3aff2aa039c/signal.json","text":"job_opened · Customer Success Engineer (contract based) · signal_desk=hiring · occurred_at=2026-04-17T08:53:46.757+00:00 · url=https://jobs.ashbyhq.com/friendliai/11f5ea91-9775-48a8-aca6-0438de737fd7 · raw={\"location\":\"Seoul\",\"team\":\"Applied Engineering\",\"ats\":\"ashby\"}"},{"ref":"E8","kind":"event","title":"Software Engineer - Senior Backend ","date":"2026-04-15T17:57:22.176+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/d5288f06-df73-407d-9d32-3eb96d735a51","signal_url":"https://onlylabs.fyi/signals/4c1b737c-6ace-410d-800d-c2b4f37f6493","signal_json_url":"https://onlylabs.fyi/signals/4c1b737c-6ace-410d-800d-c2b4f37f6493/signal.json","text":"job_opened · Software Engineer - Senior Backend  · signal_desk=hiring · occurred_at=2026-04-15T17:57:22.176+00:00 · url=https://jobs.ashbyhq.com/friendliai/d5288f06-df73-407d-9d32-3eb96d735a51 · raw={\"location\":\"San Francisco\",\"team\":\"Core Product\",\"ats\":\"ashby\"}"},{"ref":"E9","kind":"event","title":"Software Engineer – Python Developer Tools","date":"2026-04-15T17:47:21.407+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/4cfe5d9f-d34f-4100-98b2-267c10238737","signal_url":"https://onlylabs.fyi/signals/38be923b-960a-48ff-acb5-8354a1792102","signal_json_url":"https://onlylabs.fyi/signals/38be923b-960a-48ff-acb5-8354a1792102/signal.json","text":"job_opened · Software Engineer – Python Developer Tools · signal_desk=hiring · occurred_at=2026-04-15T17:47:21.407+00:00 · url=https://jobs.ashbyhq.com/friendliai/4cfe5d9f-d34f-4100-98b2-267c10238737 · raw={\"location\":\"San Francisco\",\"team\":\"Applied Engineering\",\"ats\":\"ashby\"}"},{"ref":"E10","kind":"event","title":"Software Engineer – AI Agents","date":"2026-04-15T17:37:36.461+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/6b7dbaf7-8751-402e-b253-ad968f7dc362","signal_url":"https://onlylabs.fyi/signals/4c06a497-5545-44d8-84e6-9847f13e9893","signal_json_url":"https://onlylabs.fyi/signals/4c06a497-5545-44d8-84e6-9847f13e9893/signal.json","text":"job_opened · Software Engineer – AI Agents · signal_desk=hiring · occurred_at=2026-04-15T17:37:36.461+00:00 · url=https://jobs.ashbyhq.com/friendliai/6b7dbaf7-8751-402e-b253-ad968f7dc362 · raw={\"location\":\"San Francisco\",\"team\":\"Applied Engineering\",\"ats\":\"ashby\"}"},{"ref":"E11","kind":"event","title":"Software Engineer - Full Stack","date":"2026-03-27T07:33:54.963+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/25429014-12bd-4665-a435-367d71a785c2","signal_url":"https://onlylabs.fyi/signals/bea3726d-3dd6-4535-b3d7-dcbc30470a91","signal_json_url":"https://onlylabs.fyi/signals/bea3726d-3dd6-4535-b3d7-dcbc30470a91/signal.json","text":"job_opened · Software Engineer - Full Stack · signal_desk=hiring · occurred_at=2026-03-27T07:33:54.963+00:00 · url=https://jobs.ashbyhq.com/friendliai/25429014-12bd-4665-a435-367d71a785c2 · raw={\"location\":\"San Francisco\",\"team\":\"Core Product\",\"ats\":\"ashby\"}"},{"ref":"E12","kind":"event","title":"Software Engineer – Platform Security","date":"2026-03-27T07:31:42.955+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/372dbe1e-d6e6-4726-ae09-673ec8916d05","signal_url":"https://onlylabs.fyi/signals/85f41f77-4fe7-42ff-ae50-83f014066b45","signal_json_url":"https://onlylabs.fyi/signals/85f41f77-4fe7-42ff-ae50-83f014066b45/signal.json","text":"job_opened · Software Engineer – Platform Security · signal_desk=hiring · occurred_at=2026-03-27T07:31:42.955+00:00 · url=https://jobs.ashbyhq.com/friendliai/372dbe1e-d6e6-4726-ae09-673ec8916d05 · raw={\"location\":\"San Francisco\",\"team\":\"Infrastructure\",\"ats\":\"ashby\"}"},{"ref":"E13","kind":"event","title":"QA Engineer","date":"2026-03-27T07:23:50.181+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/41859c5f-fd60-40a3-9966-39a78852008b","signal_url":"https://onlylabs.fyi/signals/0d604ae6-2c87-4d1e-9095-b7d4e0b717d3","signal_json_url":"https://onlylabs.fyi/signals/0d604ae6-2c87-4d1e-9095-b7d4e0b717d3/signal.json","text":"job_opened · QA Engineer · signal_desk=hiring · occurred_at=2026-03-27T07:23:50.181+00:00 · url=https://jobs.ashbyhq.com/friendliai/41859c5f-fd60-40a3-9966-39a78852008b · raw={\"location\":\"San Francisco\",\"team\":\"Core Product\",\"ats\":\"ashby\"}"},{"ref":"E14","kind":"event","title":"Software Engineer – AI Agents","date":"2026-03-24T14:35:58.676+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/2c922001-cccf-4006-9730-5488d4efaba6","signal_url":"https://onlylabs.fyi/signals/51e9d3f3-9d38-4051-98ef-43a4b9b20edf","signal_json_url":"https://onlylabs.fyi/signals/51e9d3f3-9d38-4051-98ef-43a4b9b20edf/signal.json","text":"job_opened · Software Engineer – AI Agents · signal_desk=hiring · occurred_at=2026-03-24T14:35:58.676+00:00 · url=https://jobs.ashbyhq.com/friendliai/2c922001-cccf-4006-9730-5488d4efaba6 · raw={\"location\":\"Seoul\",\"team\":\"Applied Engineering\",\"ats\":\"ashby\"}"},{"ref":"E15","kind":"event","title":"Solutions Architect - AI Model Specialist","date":"2026-03-15T13:51:18.989+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/b131cb30-8386-407e-a984-a75d5f2845da","signal_url":"https://onlylabs.fyi/signals/55b3273b-d25b-45df-ab70-baa42505bbfa","signal_json_url":"https://onlylabs.fyi/signals/55b3273b-d25b-45df-ab70-baa42505bbfa/signal.json","text":"job_opened · Solutions Architect - AI Model Specialist · signal_desk=hiring · occurred_at=2026-03-15T13:51:18.989+00:00 · url=https://jobs.ashbyhq.com/friendliai/b131cb30-8386-407e-a984-a75d5f2845da · raw={\"location\":\"San Francisco\",\"team\":\"Solutions Architect\",\"ats\":\"ashby\"}"},{"ref":"E16","kind":"event","title":"Solutions Architect - AI Inference Specialist","date":"2026-03-15T13:50:18.06+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/f383e666-a99a-4574-b270-4e1738734706","signal_url":"https://onlylabs.fyi/signals/651deea9-c012-4979-a17e-e534e485bf6e","signal_json_url":"https://onlylabs.fyi/signals/651deea9-c012-4979-a17e-e534e485bf6e/signal.json","text":"job_opened · Solutions Architect - AI Inference Specialist · signal_desk=hiring · occurred_at=2026-03-15T13:50:18.06+00:00 · url=https://jobs.ashbyhq.com/friendliai/f383e666-a99a-4574-b270-4e1738734706 · raw={\"location\":\"San Francisco\",\"team\":\"Solutions Architect\",\"ats\":\"ashby\"}"},{"ref":"E17","kind":"event","title":"Software Engineer – Senior Backend","date":"2026-03-15T13:31:27.577+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/5fd918c3-70c8-44f1-b080-2aef9520b312","signal_url":"https://onlylabs.fyi/signals/430133af-6e85-4c31-aa70-9b07c785d1cb","signal_json_url":"https://onlylabs.fyi/signals/430133af-6e85-4c31-aa70-9b07c785d1cb/signal.json","text":"job_opened · Software Engineer – Senior Backend · signal_desk=hiring · occurred_at=2026-03-15T13:31:27.577+00:00 · url=https://jobs.ashbyhq.com/friendliai/5fd918c3-70c8-44f1-b080-2aef9520b312 · raw={\"location\":\"Seoul\",\"team\":\"Core Product\",\"ats\":\"ashby\"}"},{"ref":"E18","kind":"event","title":"Software Engineer – GPU Kernel","date":"2026-03-15T13:19:53.768+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/cb6e7c74-c22a-4192-aec8-65769839740b","signal_url":"https://onlylabs.fyi/signals/e19e1e29-bd7d-4c15-87fb-a60d00c524e8","signal_json_url":"https://onlylabs.fyi/signals/e19e1e29-bd7d-4c15-87fb-a60d00c524e8/signal.json","text":"job_opened · Software Engineer – GPU Kernel · signal_desk=hiring · occurred_at=2026-03-15T13:19:53.768+00:00 · url=https://jobs.ashbyhq.com/friendliai/cb6e7c74-c22a-4192-aec8-65769839740b · raw={\"location\":\"San Francisco\",\"team\":\"Inference Systems\",\"ats\":\"ashby\"}"},{"ref":"E19","kind":"event","title":"Software Engineer – AI Inference Engine","date":"2026-03-15T13:17:26.899+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/eebbe221-65e4-4061-a519-8571219b6b7b","signal_url":"https://onlylabs.fyi/signals/1647eb6d-aec9-4635-b5fe-d761181e6307","signal_json_url":"https://onlylabs.fyi/signals/1647eb6d-aec9-4635-b5fe-d761181e6307/signal.json","text":"job_opened · Software Engineer – AI Inference Engine · signal_desk=hiring · occurred_at=2026-03-15T13:17:26.899+00:00 · url=https://jobs.ashbyhq.com/friendliai/eebbe221-65e4-4061-a519-8571219b6b7b · raw={\"location\":\"San Francisco\",\"team\":\"Inference Systems\",\"ats\":\"ashby\"}"},{"ref":"E20","kind":"event","title":"Senior Product Manager","date":"2026-03-15T13:07:46.886+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/friendliai/a6364382-e30a-4a30-919d-2c6b6c78185a","signal_url":"https://onlylabs.fyi/signals/9f4c0315-9126-4b6b-adba-0ed5e69ff142","signal_json_url":"https://onlylabs.fyi/signals/9f4c0315-9126-4b6b-adba-0ed5e69ff142/signal.json","text":"job_opened · Senior Product Manager · signal_desk=hiring · occurred_at=2026-03-15T13:07:46.886+00:00 · url=https://jobs.ashbyhq.com/friendliai/a6364382-e30a-4a30-919d-2c6b6c78185a · raw={\"location\":\"Seoul\",\"team\":\"Product\",\"ats\":\"ashby\"}"},{"ref":"E21","kind":"event","title":"friendliai/FAI-Model","date":"2022-05-19T08:07:53+00:00","date_source":"source","source_url":"https://github.com/friendliai/FAI-Model","signal_url":"https://onlylabs.fyi/signals/6591d9a6-b9f4-4392-9f94-56f03f8857bb","signal_json_url":"https://onlylabs.fyi/signals/6591d9a6-b9f4-4392-9f94-56f03f8857bb/signal.json","text":"repo_new · friendliai/FAI-Model · signal_desk=repos · occurred_at=2022-05-19T08:07:53+00:00 · url=https://github.com/friendliai/FAI-Model · stars=90 · raw={\"repo\":\"friendliai/FAI-Model\",\"description\":\"FriendliAI Model Hub\",\"language\":\"Python\"}"},{"ref":"E22","kind":"event","title":"friendliai/friendli-client","date":"2023-07-20T12:57:24+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client","signal_url":"https://onlylabs.fyi/signals/7eb039b0-3b52-46da-80bd-c419210c6b4a","signal_json_url":"https://onlylabs.fyi/signals/7eb039b0-3b52-46da-80bd-c419210c6b4a/signal.json","text":"repo_new · friendliai/friendli-client · signal_desk=repos · occurred_at=2023-07-20T12:57:24+00:00 · url=https://github.com/friendliai/friendli-client · stars=50 · raw={\"repo\":\"friendliai/friendli-client\",\"description\":\"[⛔️ DEPRECATED] Friendli: the fastest serving engine for generative AI\",\"language\":\"Python\"}"},{"ref":"E23","kind":"event","title":"friendliai/LLMServingPerfEvaluator","date":"2024-05-21T07:48:20+00:00","date_source":"source","source_url":"https://github.com/friendliai/LLMServingPerfEvaluator","signal_url":"https://onlylabs.fyi/signals/b147e1e7-5121-4efa-831e-40c91197b41c","signal_json_url":"https://onlylabs.fyi/signals/b147e1e7-5121-4efa-831e-40c91197b41c/signal.json","text":"repo_new · friendliai/LLMServingPerfEvaluator · signal_desk=repos · occurred_at=2024-05-21T07:48:20+00:00 · url=https://github.com/friendliai/LLMServingPerfEvaluator · stars=48 · raw={\"repo\":\"friendliai/LLMServingPerfEvaluator\",\"language\":\"Python\"}"},{"ref":"E24","kind":"event","title":"friendliai/aipm","date":"2024-05-30T09:12:51+00:00","date_source":"source","source_url":"https://github.com/friendliai/aipm","signal_url":"https://onlylabs.fyi/signals/c7f792f6-62ed-4b17-9882-c4d38b5e73c3","signal_json_url":"https://onlylabs.fyi/signals/c7f792f6-62ed-4b17-9882-c4d38b5e73c3/signal.json","text":"repo_new · friendliai/aipm · signal_desk=repos · occurred_at=2024-05-30T09:12:51+00:00 · url=https://github.com/friendliai/aipm · stars=21 · raw={\"repo\":\"friendliai/aipm\",\"description\":\"AI Agent who manages your Jira project\",\"language\":\"Python\"}"},{"ref":"E25","kind":"event","title":"friendliai/friendli-model-optimizer","date":"2024-05-28T08:18:46+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-model-optimizer","signal_url":"https://onlylabs.fyi/signals/71d0dfd8-eac3-47ed-a3aa-f3136a211561","signal_json_url":"https://onlylabs.fyi/signals/71d0dfd8-eac3-47ed-a3aa-f3136a211561/signal.json","text":"repo_new · friendliai/friendli-model-optimizer · signal_desk=repos · occurred_at=2024-05-28T08:18:46+00:00 · url=https://github.com/friendliai/friendli-model-optimizer · stars=14 · raw={\"repo\":\"friendliai/friendli-model-optimizer\",\"description\":\"FMO (Friendli Model Optimizer)\",\"language\":\"Python\"}"},{"ref":"E26","kind":"event","title":"friendliai/periflow-cli","date":"2021-12-16T07:33:16+00:00","date_source":"source","source_url":"https://github.com/friendliai/periflow-cli","signal_url":"https://onlylabs.fyi/signals/70893232-bed7-4dff-a1fc-af21a99b5fce","signal_json_url":"https://onlylabs.fyi/signals/70893232-bed7-4dff-a1fc-af21a99b5fce/signal.json","text":"repo_new · friendliai/periflow-cli · signal_desk=repos · occurred_at=2021-12-16T07:33:16+00:00 · url=https://github.com/friendliai/periflow-cli · stars=12 · raw={\"repo\":\"friendliai/periflow-cli\",\"description\":\"Welcome to PeriFlow CLI ☁︎\",\"language\":\"Python\"}"},{"ref":"E27","kind":"event","title":"friendliai/play-go-ai","date":"2024-05-25T14:59:56+00:00","date_source":"source","source_url":"https://github.com/friendliai/play-go-ai","signal_url":"https://onlylabs.fyi/signals/a4bc45dc-2c11-484c-99ec-e07aaa5c2cd1","signal_json_url":"https://onlylabs.fyi/signals/a4bc45dc-2c11-484c-99ec-e07aaa5c2cd1/signal.json","text":"repo_new · friendliai/play-go-ai · signal_desk=repos · occurred_at=2024-05-25T14:59:56+00:00 · url=https://github.com/friendliai/play-go-ai · stars=7 · raw={\"repo\":\"friendliai/play-go-ai\",\"description\":\"Golang Playground Driven by AI, Created from FriendliAI Hackathon\",\"language\":\"TypeScript\"}"},{"ref":"E28","kind":"event","title":"friendliai/examples","date":"2024-07-22T06:14:40+00:00","date_source":"source","source_url":"https://github.com/friendliai/examples","signal_url":"https://onlylabs.fyi/signals/ba9e76ea-24c8-49b5-a882-2704b08b783a","signal_json_url":"https://onlylabs.fyi/signals/ba9e76ea-24c8-49b5-a882-2704b08b783a/signal.json","text":"repo_new · friendliai/examples · signal_desk=repos · occurred_at=2024-07-22T06:14:40+00:00 · url=https://github.com/friendliai/examples · stars=6 · raw={\"repo\":\"friendliai/examples\",\"description\":\"FriendliAI Example and Tutorial Code\",\"language\":\"Jupyter Notebook\"}"},{"ref":"E29","kind":"event","title":"friendliai/llm-hackathon-tutorial","date":"2024-05-21T05:41:19+00:00","date_source":"source","source_url":"https://github.com/friendliai/llm-hackathon-tutorial","signal_url":"https://onlylabs.fyi/signals/85366b5f-180e-4f94-9b8b-ebdb3ecc6621","signal_json_url":"https://onlylabs.fyi/signals/85366b5f-180e-4f94-9b8b-ebdb3ecc6621/signal.json","text":"repo_new · friendliai/llm-hackathon-tutorial · signal_desk=repos · occurred_at=2024-05-21T05:41:19+00:00 · url=https://github.com/friendliai/llm-hackathon-tutorial · stars=6 · raw={\"repo\":\"friendliai/llm-hackathon-tutorial\",\"description\":\"FriendliAI LLM Hackathon tutorial scripts\",\"language\":\"Jupyter Notebook\"}"},{"ref":"E30","kind":"event","title":"friendliai/TensorRT-LLM","date":"2025-05-23T09:13:35+00:00","date_source":"source","source_url":"https://github.com/friendliai/TensorRT-LLM","signal_url":"https://onlylabs.fyi/signals/61a9ff48-be99-45ec-98bf-afd9a468f643","signal_json_url":"https://onlylabs.fyi/signals/61a9ff48-be99-45ec-98bf-afd9a468f643/signal.json","text":"repo_forked · friendliai/TensorRT-LLM · signal_desk=forks · occurred_at=2025-05-23T09:13:35+00:00 · url=https://github.com/friendliai/TensorRT-LLM · stars=1 · raw={\"repo\":\"friendliai/TensorRT-LLM\",\"parent\":\"NVIDIA/TensorRT-LLM\"}"},{"ref":"E31","kind":"event","title":"friendliai/huggingface-blog","date":"2025-01-22T08:53:28+00:00","date_source":"source","source_url":"https://github.com/friendliai/huggingface-blog","signal_url":"https://onlylabs.fyi/signals/16af7d95-7e27-43f1-aeda-5ff11c8177ba","signal_json_url":"https://onlylabs.fyi/signals/16af7d95-7e27-43f1-aeda-5ff11c8177ba/signal.json","text":"repo_forked · friendliai/huggingface-blog · signal_desk=forks · occurred_at=2025-01-22T08:53:28+00:00 · url=https://github.com/friendliai/huggingface-blog · stars=1 · raw={\"repo\":\"friendliai/huggingface-blog\",\"parent\":\"huggingface/blog\"}"},{"ref":"E32","kind":"event","title":"friendliai/friendli-python","date":"2024-09-03T05:29:17+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python","signal_url":"https://onlylabs.fyi/signals/56933ee9-6f23-4067-93d5-02b4b9492d1e","signal_json_url":"https://onlylabs.fyi/signals/56933ee9-6f23-4067-93d5-02b4b9492d1e/signal.json","text":"repo_new · friendliai/friendli-python · signal_desk=repos · occurred_at=2024-09-03T05:29:17+00:00 · url=https://github.com/friendliai/friendli-python · stars=1 · raw={\"repo\":\"friendliai/friendli-python\",\"description\":\"Friendli Suite python SDK\",\"language\":\"Python\"}"},{"ref":"E33","kind":"event","title":"friendliai/design-token-transformer","date":"2024-07-19T08:41:22+00:00","date_source":"source","source_url":"https://github.com/friendliai/design-token-transformer","signal_url":"https://onlylabs.fyi/signals/c19a5f8f-7de6-4aaa-9002-ac5348848b56","signal_json_url":"https://onlylabs.fyi/signals/c19a5f8f-7de6-4aaa-9002-ac5348848b56/signal.json","text":"repo_new · friendliai/design-token-transformer · signal_desk=repos · occurred_at=2024-07-19T08:41:22+00:00 · url=https://github.com/friendliai/design-token-transformer · stars=1 · raw={\"repo\":\"friendliai/design-token-transformer\"}"},{"ref":"E34","kind":"event","title":"friendliai/lm-evaluation-harness","date":"2023-08-18T07:03:21+00:00","date_source":"source","source_url":"https://github.com/friendliai/lm-evaluation-harness","signal_url":"https://onlylabs.fyi/signals/fd891caa-579d-4482-ad09-ff5a594abf45","signal_json_url":"https://onlylabs.fyi/signals/fd891caa-579d-4482-ad09-ff5a594abf45/signal.json","text":"repo_forked · friendliai/lm-evaluation-harness · signal_desk=forks · occurred_at=2023-08-18T07:03:21+00:00 · url=https://github.com/friendliai/lm-evaluation-harness · stars=1 · raw={\"repo\":\"friendliai/lm-evaluation-harness\",\"parent\":\"EleutherAI/lm-evaluation-harness\"}"},{"ref":"E35","kind":"event","title":"friendliai/pi-mono","date":"2026-02-23T21:55:32+00:00","date_source":"source","source_url":"https://github.com/friendliai/pi-mono","signal_url":"https://onlylabs.fyi/signals/d5e8b4cc-440a-4fc4-9405-5a35a619d275","signal_json_url":"https://onlylabs.fyi/signals/d5e8b4cc-440a-4fc4-9405-5a35a619d275/signal.json","text":"repo_forked · friendliai/pi-mono · signal_desk=forks · occurred_at=2026-02-23T21:55:32+00:00 · url=https://github.com/friendliai/pi-mono · raw={\"repo\":\"friendliai/pi-mono\",\"parent\":\"earendil-works/pi\"}"},{"ref":"E36","kind":"event","title":"friendliai/friendli-python v0.12.4","date":"2026-01-05T03:03:43+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python/releases/tag/v0.12.4","signal_url":"https://onlylabs.fyi/signals/144e3887-dd33-406b-81f7-ad0232a36c06","signal_json_url":"https://onlylabs.fyi/signals/144e3887-dd33-406b-81f7-ad0232a36c06/signal.json","text":"release · friendliai/friendli-python v0.12.4 · signal_desk=releases · occurred_at=2026-01-05T03:03:43+00:00 · url=https://github.com/friendliai/friendli-python/releases/tag/v0.12.4 · raw={\"repo\":\"friendliai/friendli-python\"}"},{"ref":"E37","kind":"event","title":"friendliai/opencode","date":"2025-12-27T06:34:52+00:00","date_source":"source","source_url":"https://github.com/friendliai/opencode","signal_url":"https://onlylabs.fyi/signals/83de12a5-7e2d-4487-870a-293656ca217d","signal_json_url":"https://onlylabs.fyi/signals/83de12a5-7e2d-4487-870a-293656ca217d/signal.json","text":"repo_forked · friendliai/opencode · signal_desk=forks · occurred_at=2025-12-27T06:34:52+00:00 · url=https://github.com/friendliai/opencode · raw={\"repo\":\"friendliai/opencode\",\"parent\":\"anomalyco/opencode\"}"},{"ref":"E38","kind":"event","title":"friendliai/friendli-python v0.12.3","date":"2025-12-24T02:01:54+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python/releases/tag/v0.12.3","signal_url":"https://onlylabs.fyi/signals/f07216e5-d101-4764-a946-bb4609f6786c","signal_json_url":"https://onlylabs.fyi/signals/f07216e5-d101-4764-a946-bb4609f6786c/signal.json","text":"release · friendliai/friendli-python v0.12.3 · signal_desk=releases · occurred_at=2025-12-24T02:01:54+00:00 · url=https://github.com/friendliai/friendli-python/releases/tag/v0.12.3 · raw={\"repo\":\"friendliai/friendli-python\"}"},{"ref":"E39","kind":"event","title":"friendliai/models.dev","date":"2025-12-23T06:50:56+00:00","date_source":"source","source_url":"https://github.com/friendliai/models.dev","signal_url":"https://onlylabs.fyi/signals/c37c2c73-15a9-4af6-a642-31cdfd565eb4","signal_json_url":"https://onlylabs.fyi/signals/c37c2c73-15a9-4af6-a642-31cdfd565eb4/signal.json","text":"repo_forked · friendliai/models.dev · signal_desk=forks · occurred_at=2025-12-23T06:50:56+00:00 · url=https://github.com/friendliai/models.dev · raw={\"repo\":\"friendliai/models.dev\",\"parent\":\"anomalyco/models.dev\"}"},{"ref":"E40","kind":"event","title":"friendliai/friendli-python v0.12.2","date":"2025-12-02T06:19:01+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python/releases/tag/v0.12.2","signal_url":"https://onlylabs.fyi/signals/32e7242b-7592-48e5-95dc-3630713913c8","signal_json_url":"https://onlylabs.fyi/signals/32e7242b-7592-48e5-95dc-3630713913c8/signal.json","text":"release · friendliai/friendli-python v0.12.2 · signal_desk=releases · occurred_at=2025-12-02T06:19:01+00:00 · url=https://github.com/friendliai/friendli-python/releases/tag/v0.12.2 · raw={\"repo\":\"friendliai/friendli-python\"}"},{"ref":"E41","kind":"event","title":"friendliai/friendli-python v0.12.1","date":"2025-12-02T00:57:49+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python/releases/tag/v0.12.1","signal_url":"https://onlylabs.fyi/signals/caa0fd41-f024-4b78-b01c-25d318a9d696","signal_json_url":"https://onlylabs.fyi/signals/caa0fd41-f024-4b78-b01c-25d318a9d696/signal.json","text":"release · friendliai/friendli-python v0.12.1 · signal_desk=releases · occurred_at=2025-12-02T00:57:49+00:00 · url=https://github.com/friendliai/friendli-python/releases/tag/v0.12.1 · raw={\"repo\":\"friendliai/friendli-python\"}"},{"ref":"E42","kind":"event","title":"friendliai/friendli-python v0.11.0","date":"2025-09-05T05:18:07+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-python/releases/tag/v0.11.0","signal_url":"https://onlylabs.fyi/signals/cdf169f0-5c14-40a6-aafd-d274c6f97dbd","signal_json_url":"https://onlylabs.fyi/signals/cdf169f0-5c14-40a6-aafd-d274c6f97dbd/signal.json","text":"release · friendliai/friendli-python v0.11.0 · signal_desk=releases · occurred_at=2025-09-05T05:18:07+00:00 · url=https://github.com/friendliai/friendli-python/releases/tag/v0.11.0 · raw={\"repo\":\"friendliai/friendli-python\"}"},{"ref":"E43","kind":"event","title":"friendliai/friendli-gradio","date":"2025-08-20T08:00:05+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-gradio","signal_url":"https://onlylabs.fyi/signals/69396b58-e34a-4a48-82dc-259b33eb9727","signal_json_url":"https://onlylabs.fyi/signals/69396b58-e34a-4a48-82dc-259b33eb9727/signal.json","text":"repo_new · friendliai/friendli-gradio · signal_desk=repos · occurred_at=2025-08-20T08:00:05+00:00 · url=https://github.com/friendliai/friendli-gradio · raw={\"repo\":\"friendliai/friendli-gradio\",\"language\":\"Python\"}"},{"ref":"E44","kind":"event","title":"friendliai/flashinfer","date":"2025-07-09T05:28:48+00:00","date_source":"source","source_url":"https://github.com/friendliai/flashinfer","signal_url":"https://onlylabs.fyi/signals/82b51659-b649-4121-af58-1e3781cbf4dc","signal_json_url":"https://onlylabs.fyi/signals/82b51659-b649-4121-af58-1e3781cbf4dc/signal.json","text":"repo_forked · friendliai/flashinfer · signal_desk=forks · occurred_at=2025-07-09T05:28:48+00:00 · url=https://github.com/friendliai/flashinfer · raw={\"repo\":\"friendliai/flashinfer\",\"parent\":\"flashinfer-ai/flashinfer\"}"},{"ref":"E45","kind":"event","title":"friendliai/cutlass","date":"2025-07-08T10:38:38+00:00","date_source":"source","source_url":"https://github.com/friendliai/cutlass","signal_url":"https://onlylabs.fyi/signals/86fd025d-b8fd-4932-b5ac-5beee1dd6432","signal_json_url":"https://onlylabs.fyi/signals/86fd025d-b8fd-4932-b5ac-5beee1dd6432/signal.json","text":"repo_forked · friendliai/cutlass · signal_desk=forks · occurred_at=2025-07-08T10:38:38+00:00 · url=https://github.com/friendliai/cutlass · raw={\"repo\":\"friendliai/cutlass\",\"parent\":\"NVIDIA/cutlass\"}"},{"ref":"E46","kind":"event","title":"friendliai/tokasaurus","date":"2025-06-06T10:14:59+00:00","date_source":"source","source_url":"https://github.com/friendliai/tokasaurus","signal_url":"https://onlylabs.fyi/signals/5f07edc7-9971-435b-b270-39a4a2db224b","signal_json_url":"https://onlylabs.fyi/signals/5f07edc7-9971-435b-b270-39a4a2db224b/signal.json","text":"repo_forked · friendliai/tokasaurus · signal_desk=forks · occurred_at=2025-06-06T10:14:59+00:00 · url=https://github.com/friendliai/tokasaurus · raw={\"repo\":\"friendliai/tokasaurus\",\"parent\":\"ScalingIntelligence/tokasaurus\"}"},{"ref":"E47","kind":"event","title":"friendliai/friendli-client v1.5.7","date":"2025-01-24T03:10:55+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client/releases/tag/v1.5.7","signal_url":"https://onlylabs.fyi/signals/32af51d2-8d83-48c3-9091-0b40587064a7","signal_json_url":"https://onlylabs.fyi/signals/32af51d2-8d83-48c3-9091-0b40587064a7/signal.json","text":"release · friendliai/friendli-client v1.5.7 · signal_desk=releases · occurred_at=2025-01-24T03:10:55+00:00 · url=https://github.com/friendliai/friendli-client/releases/tag/v1.5.7 · raw={\"repo\":\"friendliai/friendli-client\"}"},{"ref":"E48","kind":"event","title":"friendliai/gorilla","date":"2025-01-14T05:48:27+00:00","date_source":"source","source_url":"https://github.com/friendliai/gorilla","signal_url":"https://onlylabs.fyi/signals/fa64eecc-12c5-4ac9-b6ab-ba4ecbb1e6cb","signal_json_url":"https://onlylabs.fyi/signals/fa64eecc-12c5-4ac9-b6ab-ba4ecbb1e6cb/signal.json","text":"repo_forked · friendliai/gorilla · signal_desk=forks · occurred_at=2025-01-14T05:48:27+00:00 · url=https://github.com/friendliai/gorilla · raw={\"repo\":\"friendliai/gorilla\",\"parent\":\"ShishirPatil/gorilla\"}"},{"ref":"E49","kind":"event","title":"friendliai/friendli-model-optimizer v0.10.0","date":"2025-01-08T09:55:33+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.10.0","signal_url":"https://onlylabs.fyi/signals/b58099bd-d50d-43cc-b7e4-60c8452b7dcc","signal_json_url":"https://onlylabs.fyi/signals/b58099bd-d50d-43cc-b7e4-60c8452b7dcc/signal.json","text":"release · friendliai/friendli-model-optimizer v0.10.0 · signal_desk=releases · occurred_at=2025-01-08T09:55:33+00:00 · url=https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.10.0 · raw={\"repo\":\"friendliai/friendli-model-optimizer\"}"},{"ref":"E50","kind":"event","title":"friendliai/friendli-model-optimizer v0.9.0","date":"2025-01-03T10:00:07+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.9.0","signal_url":"https://onlylabs.fyi/signals/d9f3f216-2ddd-4c61-8725-732a8afcda1a","signal_json_url":"https://onlylabs.fyi/signals/d9f3f216-2ddd-4c61-8725-732a8afcda1a/signal.json","text":"release · friendliai/friendli-model-optimizer v0.9.0 · signal_desk=releases · occurred_at=2025-01-03T10:00:07+00:00 · url=https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.9.0 · raw={\"repo\":\"friendliai/friendli-model-optimizer\"}"},{"ref":"E51","kind":"event","title":"friendliai/friendli-model-optimizer v0.8.0","date":"2024-10-31T08:13:29+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.8.0","signal_url":"https://onlylabs.fyi/signals/63060855-2cce-4190-8877-0e8fa4015e1b","signal_json_url":"https://onlylabs.fyi/signals/63060855-2cce-4190-8877-0e8fa4015e1b/signal.json","text":"release · friendliai/friendli-model-optimizer v0.8.0 · signal_desk=releases · occurred_at=2024-10-31T08:13:29+00:00 · url=https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.8.0 · raw={\"repo\":\"friendliai/friendli-model-optimizer\"}"},{"ref":"E52","kind":"event","title":"friendliai/simple-evals-archived","date":"2024-10-27T10:12:37+00:00","date_source":"source","source_url":"https://github.com/friendliai/simple-evals-archived","signal_url":"https://onlylabs.fyi/signals/24fe0b99-cd4e-48d8-bf45-c87d2c452ec1","signal_json_url":"https://onlylabs.fyi/signals/24fe0b99-cd4e-48d8-bf45-c87d2c452ec1/signal.json","text":"repo_forked · friendliai/simple-evals-archived · signal_desk=forks · occurred_at=2024-10-27T10:12:37+00:00 · url=https://github.com/friendliai/simple-evals-archived · raw={\"repo\":\"friendliai/simple-evals-archived\",\"parent\":\"openai/simple-evals\"}"},{"ref":"E53","kind":"event","title":"friendliai/friendli-client v1.5.6","date":"2024-10-17T04:57:48+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client/releases/tag/v1.5.6","signal_url":"https://onlylabs.fyi/signals/05885393-2bb2-4ce9-bf7e-fe44cb5263bf","signal_json_url":"https://onlylabs.fyi/signals/05885393-2bb2-4ce9-bf7e-fe44cb5263bf/signal.json","text":"release · friendliai/friendli-client v1.5.6 · signal_desk=releases · occurred_at=2024-10-17T04:57:48+00:00 · url=https://github.com/friendliai/friendli-client/releases/tag/v1.5.6 · raw={\"repo\":\"friendliai/friendli-client\"}"},{"ref":"E54","kind":"event","title":"friendliai/friendli-client v1.5.5","date":"2024-10-15T07:47:38+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client/releases/tag/v1.5.5","signal_url":"https://onlylabs.fyi/signals/19859fb6-c904-4962-813c-6580f4fe2002","signal_json_url":"https://onlylabs.fyi/signals/19859fb6-c904-4962-813c-6580f4fe2002/signal.json","text":"release · friendliai/friendli-client v1.5.5 · signal_desk=releases · occurred_at=2024-10-15T07:47:38+00:00 · url=https://github.com/friendliai/friendli-client/releases/tag/v1.5.5 · raw={\"repo\":\"friendliai/friendli-client\"}"},{"ref":"E55","kind":"event","title":"friendliai/friendli-model-optimizer v0.7.0","date":"2024-09-25T06:56:37+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.7.0","signal_url":"https://onlylabs.fyi/signals/80f9c0eb-e26f-4e22-9224-cc915f85973b","signal_json_url":"https://onlylabs.fyi/signals/80f9c0eb-e26f-4e22-9224-cc915f85973b/signal.json","text":"release · friendliai/friendli-model-optimizer v0.7.0 · signal_desk=releases · occurred_at=2024-09-25T06:56:37+00:00 · url=https://github.com/friendliai/friendli-model-optimizer/releases/tag/v0.7.0 · raw={\"repo\":\"friendliai/friendli-model-optimizer\"}"},{"ref":"E56","kind":"event","title":"friendliai/friendli-client v1.5.4","date":"2024-08-30T07:37:02+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client/releases/tag/v1.5.4","signal_url":"https://onlylabs.fyi/signals/7bbe6532-05d3-4f38-8121-88da43e7cccc","signal_json_url":"https://onlylabs.fyi/signals/7bbe6532-05d3-4f38-8121-88da43e7cccc/signal.json","text":"release · friendliai/friendli-client v1.5.4 · signal_desk=releases · occurred_at=2024-08-30T07:37:02+00:00 · url=https://github.com/friendliai/friendli-client/releases/tag/v1.5.4 · raw={\"repo\":\"friendliai/friendli-client\"}"},{"ref":"E57","kind":"event","title":"friendliai/LLMServingPerfEvaluator v0.0.5","date":"2024-08-28T09:09:32+00:00","date_source":"source","source_url":"https://github.com/friendliai/LLMServingPerfEvaluator/releases/tag/v0.0.5","signal_url":"https://onlylabs.fyi/signals/3bb869b4-49c4-4855-989c-ee4a7c23fa38","signal_json_url":"https://onlylabs.fyi/signals/3bb869b4-49c4-4855-989c-ee4a7c23fa38/signal.json","text":"release · friendliai/LLMServingPerfEvaluator v0.0.5 · signal_desk=releases · occurred_at=2024-08-28T09:09:32+00:00 · url=https://github.com/friendliai/LLMServingPerfEvaluator/releases/tag/v0.0.5 · raw={\"repo\":\"friendliai/LLMServingPerfEvaluator\"}"},{"ref":"E58","kind":"event","title":"friendliai/friendli-client v1.5.3","date":"2024-08-23T07:03:30+00:00","date_source":"source","source_url":"https://github.com/friendliai/friendli-client/releases/tag/v1.5.3","signal_url":"https://onlylabs.fyi/signals/585d026e-cc32-4d0b-8abd-fa29d26d0503","signal_json_url":"https://onlylabs.fyi/signals/585d026e-cc32-4d0b-8abd-fa29d26d0503/signal.json","text":"release · friendliai/friendli-client v1.5.3 · signal_desk=releases · occurred_at=2024-08-23T07:03:30+00:00 · url=https://github.com/friendliai/friendli-client/releases/tag/v1.5.3 · raw={\"repo\":\"friendliai/friendli-client\"}"},{"ref":"E59","kind":"event","title":"friendliai/atlas-go-sdk v0.5.11","date":"2024-08-21T08:04:15+00:00","date_source":"source","source_url":"https://github.com/friendliai/atlas-go-sdk/releases/tag/v0.5.11","signal_url":"https://onlylabs.fyi/signals/9eb75fb4-22c5-4060-9547-6397e3fa7449","signal_json_url":"https://onlylabs.fyi/signals/9eb75fb4-22c5-4060-9547-6397e3fa7449/signal.json","text":"release · friendliai/atlas-go-sdk v0.5.11 · signal_desk=releases · occurred_at=2024-08-21T08:04:15+00:00 · url=https://github.com/friendliai/atlas-go-sdk/releases/tag/v0.5.11 · raw={\"repo\":\"friendliai/atlas-go-sdk\"}"},{"ref":"E60","kind":"event","title":"friendliai/weaviate-io","date":"2024-08-19T01:51:23+00:00","date_source":"source","source_url":"https://github.com/friendliai/weaviate-io","signal_url":"https://onlylabs.fyi/signals/8302f521-e50e-4de4-945a-951dc078e122","signal_json_url":"https://onlylabs.fyi/signals/8302f521-e50e-4de4-945a-951dc078e122/signal.json","text":"repo_forked · friendliai/weaviate-io · signal_desk=forks · occurred_at=2024-08-19T01:51:23+00:00 · url=https://github.com/friendliai/weaviate-io · raw={\"repo\":\"friendliai/weaviate-io\",\"parent\":\"weaviate/weaviate-io\"}"}]}