Site Reliability Engineer

xAIs 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 companys mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All engineers and researchers are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

About the Role

As a Data Center Site Reliability Engineer (SRE) at xAI, you will play a pivotal role in ensuring the reliability, scalability, and performance of our state-of-the-art data center infrastructure, including the Colossus supercluster in Memphisthe world's largest AI training cluster with over 100,000 liquid-cooled Nvidia GPUs and plans for expansion to 1 million. This infrastructure powers advanced AI workloads, massive-scale model training, and products like Grok, enabling breakthroughs in understanding the universe. You will collaborate with cross-functional teams to automate operations, enhance observability, and maintain high availability for large-scale distributed systems. This is a hands-on technical position in a dynamic environment, offering the opportunity to tackle complex challenges at the intersection of AI, data center operations, and software reliability.

Key Responsibilities

• Maintain and improve the reliability and uptime of xAIs on-premises and cloud-based data center environments, including high-density GPU clusters for AI training.
• Design, implement, and manage monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, PagerDuty).
• Develop and maintain infrastructure-as-code (Pulumi, Terraform) and continuous deployment pipelines (Buildkite, ArgoCD).
• Participate in on-call rotations, respond to incidents, perform root cause analysis, and drive post-mortem processes.
• Analyze system performance, forecast capacity needs, and optimize resource utilization for massive AI / ML workloads.
• Collaborate with hardware, networking, and software engineering teams to design and implement resilient, scalable solutions, such as RDMA fabrics and liquid-cooling systems.
• Create and maintain documentation and standard operating procedures.
• Contribute to the efficiency of AI training pipelines by identifying and mitigating bottlenecks in compute, storage, and networking at unprecedented scales.

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Preferred Qualifications

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