Research Engineer, Discovery

Job Application for Research Engineer, Discovery at Anthropic

Research Engineer, Discovery San Francisco, CA

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Team

Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier.

About the role

As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity.

Responsibilities:

Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments

Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities

Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.

Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows

Collaborate to translate experimental requirements into production-ready infrastructure

Develop large scale data pipelines to handle advanced language model training requirements

Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems

Are a strong communicator and enjoy working collaboratively

Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads

Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale

Have proven track record of building large-scale data pipelines and distributed storage systems

Excel at diagnosing and resolving complex infrastructure challenges in production environments

Can work effectively across the full ML stack from data pipelines to performance optimization

Have experience collaborating with other researchers to scale experimental ideas

Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)

Background in building infrastructure for AI research labs or large-scale ML organizations

Knowledge of GPU/TPU architectures and language model inference optimization

Experience with cloud platforms (AWS, GCP) at enterprise scale

Familiarity with VM and container orchestration.

Experience with workflow orchestration tools and experiment management systems

History working with large scale reinforcement learning

Comfort with large scale data pipelines (Beam, Spark, Dask, …)

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $350,000 - $850,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely…