Site Reliability Engineer

About the Role

Maintain and improve the reliability and uptime of xAI’s 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.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
• 5+ years in site reliability engineering, data center operations, or large-scale infrastructure management.
• Expert-level knowledge of Kubernetes (on-prem and cloud), infrastructure-as-code tools (Pulumi, Terraform), and CI/CD systems (Buildkite, ArgoCD).
• Proficiency in at least one systems programming language (Rust, C++, Go) and strong scripting/automation skills.
• Deep understanding of monitoring and observability technologies.
• Strong troubleshooting skills across hardware, networking, and distributed software systems.
• Proven experience with incident response, including on-call rotations, rapid incident resolution, root cause analysis, and implementation of preventative measures.
• Excellent communication and documentation skills, with the ability to share knowledge concisely and accurately.

Preferred Skills and Experience

• Experience supporting AI/ML workloads or high-density compute environments, including large-scale GPU clusters and HPC systems.
• Familiarity with data center electrical, cooling, and network systems, such as liquid-cooling and high-bandwidth interconnects.
• Certifications in SRE, Kubernetes, or data center operations.
• Experience with both on-premises and cloud infrastructure at scale.