Sr. Software Engineer (Data Center Automation)

Job Application for Sr. Software Engineer (Data Center Automation) at xAI

Sr. Software Engineer (Data Center Automation) Memphis, TN

ABOUT xAI

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

ABOUT THE ROLE:

We are seeking a highly skilled Sr. Software Engineer to join our team in managing and enhancing reliability across a multi-data center environment. This role focuses on automating processes, building and implementing robust observability solutions, and ensuring seamless operations for mission-critical AI infrastructure. The ideal candidate will combine strong coding abilities with hands-on data center experience to build scalable reliability services, optimize system performance, and minimize downtime—including close partnership with facility operations to address physical infrastructure impacts. If you thrive in lightning-fast, distributed environments and are passionate about leveraging automation to drive efficiency, this is an opportunity to make a significant impact on our infrastructure's resilience and scalability.

In an era where AI workloads demand near-zero downtime, this position plays a pivotal role in bridging software engineering principles with physical data center realities. By prioritizing automation and observability, team members in this role can reduce mean time to recovery (MTTR) by up to 50% through proactive monitoring and automated remediation, based on industry benchmarks from high-scale environments like those at hyperscale cloud providers.

The primary objective of this team is to mitigate downtime and minimize impact to end-users from both scheduled and unscheduled maintenance, as well as events affecting onsite data centers. This is achieved through proactive automation, robust observability, and integrated software-physical reliability strategies, ensuring our AI infrastructure remains resilient, scalable, and at the cutting edge of innovation.

RESPONSIBILITIES:

Design, develop, and deploy scalable code and services (primarily in Python and Rust, with flexibility for emerging languages) to automate reliability workflows, including monitoring, alerting, incident response, and infrastructure provisioning. We value adaptability to new tools and paradigms in the fast-evolving AI space.

Implement and maintain observability tools and practices, such as metrics collection, logging, tracing, and dashboards, to provide real-time insights into system health across multiple data centers—open to innovative stacks beyond traditional ones like ELK.

Collaborate with cross-functional teams—including software development, network engineering, site operations, and facility operations (critical facilities, mechanical/electrical teams, and data center infrastructure management)—to identify reliability bottlenecks, automate solutions for fault tolerance, disaster recovery, capacity planning, and physical/environmental risk mitigation (e.g., power redundancy, cooling efficiency, and environmental monitoring integration).This role encourages broad skill sets from diverse technical backgrounds to foster innovation.

Troubleshoot and resolve complex issues in data center environments, including hardware failures, environmental anomalies, software bugs, and network-related problems, while adhering to reliability principles like error budgets and SLAs.** Key Insight: By applying SWE rigor to troubleshooting, team members can create reusable diagnostic tools that accelerate resolution, turning unscheduled events (e.g., hardware faults) into opportunities for system hardening and reducing overall end-user impact through targeted SLAs that prioritize critical AI services. We seek versatile problem-solvers who adapt to bleeding-edge challenges.

Optimize Linux-based systems for performance, security, and reliability, including kernel tuning, container orchestration (e.g., Kubernetes or emerging alternatives), and scripting for automation.

Understand network topologies and concepts in large-scale, multi-data center environments to effectively troubleshoot connectivity, routing, redundancy, and performance issues; integrate observability into data center interconnects and facility-level controls for rapid diagnosis and automation.** Key Insight: In multi-site setups, network insights allow for automated failover mechanisms that handle both digital and physical disruptions, ensuring seamless continuity for end-users during events like fiber cuts or power outages. This attracts candidates from varied networking and systems backgrounds to drive forward-thinking solutions.

Participate in on-call rotations, post-incident reviews (blameless postmortems), and continuous improvement initiatives to enhance overall site reliability, including joint exercises with facility teams for physical failover and recovery scenarios. We prioritize growth-minded individuals who embrace evolving practices.

Mentor junior team members and document processes to foster a culture of automation, knowledge sharing, and adaptability to new technologies.

BASIC QUALIFICATIONS:

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a closely related technical field (or equivalent professional experience).

3+ years of hands-on experience in site reliability engineering (SRE), infrastructure engineering, DevOps, or systems engineering, preferably supporting large-scale, distributed, or production environments.

Strong programming skills with proven production experience in Python (required for automation and tooling); experience with Rust or willingness to work in Rust is a plus, but strong coding fundamentals in at least one systems-level language (e.g., Python, Go, C++) are essential.

Solid experience with Linux systems administration, performance tuning, kernel-level…