Member of Technical Staff, Site Reliability Engineer (HPC) · Microsoft

Sun, 08 Mar 2026 22:09:23 GMT
Engineering

Microsoft · Mountain View · $139,900 – $274,800 per year · hybrid
As Microsoft continues to push the boundaries of AI, we are on the lookout for experienced individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. We're looking for an experienced HPC Site Reliability Engineer (SRE) to join our High Performance Computing (HPC) infrastructure team. In this role, you'll blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You'll ensure that AI systems stay efficient and reliable with very high uptimes. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. This role is part of Microsoft AI's Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. Responsibilities Reliability & Availability : Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference. Observability : Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into all aspects of HPC systems including GPU, clusters, storage and networking. Automation & Tooling : Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments. Incident Management : Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements. Security & Compliance : Ensure data privacy, compliance, and secure operations across model training and serving environments. Collaboration : Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows. Qualifications Required Qualifications Master’s Degree in Computer Science, Information Technology, or related field AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR Bachelor’s Degree in Computer Science, Information Technology, or related field AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR equivalent experience Preferred Qualifications Strong proficiency in Kubernetes, Docker, and container orchestration. Knowledge of CI/CD pipelines for Inference and ML model deployment. Hands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code. Expertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.). Strong programming/scripting skills in Python, Go, or Bash. Solid knowledge of distributed systems, networking, and storage. Experience running large-scale GPU clusters for ML/AI workloads (preferred). Familiarity with ML training/inference pipelines. Experience with high-performance computing (HPC) and workload schedulers (Kubernetes operators). Background in capacity planning & cost optimization for GPU-heavy environments. Work on cutting-edge infrastructure that powers the future of Generative AI. Collaborate with world-class researchers and engineers. Impact millions of users through reliable and responsible AI deployments. Competitive compensation, equity options, and comprehensive benefits.