HPC Operations Engineering Manager

Microsoft AI is seeking  an experienced  High Performance Computing Operations Engineering Manager  to join our infrastructure team on the MAI SuperIntelligence Team. In this role, you’ll lead a team of Site Reliability Engineers who blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You’ll work closely with ML researchers, data engineers, and product developers to design and operate the platforms that power training, fine-tuning, and serving generative AI models.     Microsoft Superintelligence Team     Microsoft Superintelligence Team’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. We’re also fortunate to partner with incredible product  teams  giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in — come and join us as we work on our next generation of models!    By applying to this Mountain View, CA position, you are required to be local to the San Francisco area and in office 4 days a week.

Responsibilities   Team leadership : Lead a team of experienced SREs to ensure uptime, resiliency and fault tolerance of AI model training and inference systems.    Observability : Design and help maintain monitoring, alerting, and logging systems to provide real-time visibility into model serving pipelines and infra.   Automation & Tooling : Lead building of automation for deployments, incident response, scaling, and failover in hybrid cloud/on-prem 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.

Required Qualifications Bachelor's Degree in Computer Science  or related technical field AND 8+ years technical engineering experience with Site Reliability Engineering, DevOps, or Infrastructure Engineering Leadership roles AND 8+  years experience  with Kubernetes, Docker, and container orchestration, AND 6+  years experience  with programming/scripting skills not limited to Python, Go, or Bash    OR  equivalent  experience   Preferred Qualifications: Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience AND 10+ years experience with Kubernetes, Docker, and container orchestration, AND 10+ years' experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code OR equivalent experience 6+ years people management experience.   8+  years experience  in monitoring & observability tools (Grafana, Datadog,  OpenTelemetry , etc.).   Knowledge of  CI/CD pipelines  for Inference and ML model deployment.   Solid knowledge