Microsoft
Posted 71 days agoVerified live 2d ago
Site Reliability Engineer (HPC)
Brief overview
Remote
MastersOr in progress
2+ yrsMinimum
7,292 H-1B approvalsDept. of Labor
7,082 green cardsCertified filings
Site Reliability EngineeringDevOpsInfrastructure EngineeringKubernetesDockerContainer OrchestrationCI/CD PipelinesAzureAWSGCPInfrastructure-as-CodeGrafanaDatadogOpenTelemetryPythonGoBash
About the company
Microsoftmicrosoft.com
Microsoft is a software corporation that develops, manufactures, licenses, supports, and sells a range of software products and services.
Visa sponsorship history
4 years sponsoring, last filed FY2026
Data powered by U.S. Department of Labor. This does not guarantee sponsorship for this specific role.7,292H-1B approved
97%approval rate
1,172new H-1B hires
7,082PERM certified
$169,178median wage / yr
H-1B Petition ApprovalsVisas USCIS actually granted: the strongest sign the company sponsors.
20232,866
20241,446
20251,801
20261,179
LCA Certified ApplicationsAn early filing step, not a visa approval: it signals intent, not confirmed sponsorship.
20231,912
20242,842
20251,798
20261,338
Green Card (PERM) FilingsCertified green card filings: a long-term commitment to international hires.
20232,396
20241,662
20252,928
202696
Top sponsored roles
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Sponsored employees from
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Job description
Summary
Microsoft is a leading technology company focused on advancing artificial intelligence. They are seeking an experienced HPC Site Reliability Engineer to join their High Performance Computing infrastructure team, responsible for ensuring the reliability and efficiency of large-scale distributed AI systems.
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
- Competitive compensation, equity options, and comprehensive benefits
Qualifications
Must Haves
- 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
Nice to Haves
- 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
