Member of Technical Staff

xAI • Memphis, TN

8d

About The Position

xAI is seeking a highly skilled Member of Technical Staff to join their 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. 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. 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.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a closely related technical field (or equivalent professional experience).
• 5+ 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 understanding, and scripting/automation in production environments.
• Practical knowledge of containerization and orchestration technologies, such as Docker and Kubernetes (or similar systems).
• Experience implementing observability solutions, including metrics, logging, tracing, monitoring tools (e.g., Prometheus, Grafana, or alternatives), alerting, and dashboards.
• Familiarity with troubleshooting complex issues in distributed systems, including software bugs, hardware failures, network problems, and environmental factors.
• Understanding of networking fundamentals (TCP/IP, routing, redundancy, DNS) in large-scale or multi-site environments.
• Experience participating in on-call rotations, incident response, post-incident reviews (blameless postmortems), and reliability practices such as error budgets or SLAs.
• Ability to collaborate effectively with cross-functional teams (software engineers, network teams, site/facility operations, mechanical/electrical teams).

Nice To Haves

• 7+ years of experience in SRE or infrastructure roles, ideally in hyperscale, cloud, or AI/ML training infrastructure environments with multi-data center setups.
• Hands-on experience operating or scaling Kubernetes clusters (or equivalent orchestration) at large scale, including automation for provisioning, lifecycle management, and high-availability.
• Proficiency in Rust for systems programming and performance-critical components.
• Direct experience integrating software reliability tools with physical data center infrastructure (e.g., power, cooling, environmental monitoring, facility controls) and automating responses to physical events.
• Exposure to advanced or innovative observability stacks beyond traditional tools (e.g., exploring cutting-edge alternatives for metrics, logs, and tracing).
• Experience building automated remediation, fault tolerance, disaster recovery, capacity planning, or predictive failure detection systems.
• Background in optimizing Linux-based systems for AI workloads, GPU clusters, or high-throughput compute environments.
• Demonstrated success reducing downtime, MTTR, or improving resource efficiency (e.g., through automation or observability) in high-stakes production settings.
• Prior work with bare-metal provisioning, data center interconnects, or hybrid/multi-site failover mechanisms.
• Mentoring experience, strong documentation skills, and a track record of fostering knowledge sharing and automation culture.
• Comfort with rapid technology adaptation in fast-evolving domains like AI infrastructure.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree