MTS - Site Reliability Engineer at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Mar, 26

Salary

274800.0

Posted On

18 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Site Reliability Engineering, DevOps, Infrastructure Engineering, Kubernetes, Docker, CI/CD Pipelines, Public Cloud Platforms, Infrastructure-as-Code, Monitoring Tools, Observability Tools, Python, Go, Bash, Distributed Systems, Networking, Storage

Industry

Software Development

Description

Overview 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. Microsoft’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. Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. 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. 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. Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 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 $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

Ensure uptime, resiliency, and fault tolerance of AI model training and inference systems while designing and maintaining monitoring and alerting systems. Collaborate with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.