Note: The job is a remote job and is open to candidates in USA. Microsoft is a leading technology company focused on pushing the boundaries of AI. They are seeking an experienced HPC Site Reliability Engineer to ensure the reliability and efficiency of their distributed AI infrastructure while collaborating with teams to enhance developer experiences.
Responsibilities
- Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference
- 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
- Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments
- Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements
- Ensure data privacy, compliance, and secure operations across model training and serving environments
- Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows
Skills
- 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
- 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
Benefits
- Competitive compensation, equity options, and comprehensive benefits.
- Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
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