Note: The job is a remote job and is open to candidates in USA. Microsoft is seeking an experienced High Performance Computing Operations Engineering Manager to join their infrastructure team on the MAI SuperIntelligence Team. In this role, you will lead a team of Site Reliability Engineers to maintain the reliability and efficiency of large-scale distributed AI infrastructure.
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
Skills
- 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
- 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 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
Benefits
- Certain roles may be eligible for benefits and other compensation.
- Find additional benefits and pay information here:
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