Description
As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad , to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It’s also inclusive: we aim to make AI accessible to all , consumers, businesses, developers , so that everyone can realize its benefits.
We’re looking for an experienced Site Reliability Engineer (SRE) to join our 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 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.
Responsibilities
- Reliability & Availability: Ensure uptime, resiliency, and fault tolerance of AI model training and inference systems.
- Observability: Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into model serving pipelines and infra.
- Performance Optimization: Analyze system performance and scalability, optimize resource utilization (compute, GPU clusters, storage, networking).
- Automation & Tooling: Build 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.
Qualifications
Required Qualifications
- 4+ years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering roles.
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.
Benefits
- Competitive compensation, equity options, and comprehensive benefits.
Salary
The typical base pay range for this role across the U.S. is USD $119,800.00 – $234,700.00 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200.00 – $261,000.00 per year.