Job 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 AI Reliability Engineer to join our High Performance Computing (HPC) 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 ensure that AI systems stay efficient and reliable with very high uptimes. Microsoft AI This role is part of Microsoft AI. Our Superintelligence team is a startup-like organization within Microsoft, dedicated to pushing the boundaries of artificial intelligence while maintaining a strong commitment to safety, responsibility, and human values. Our mission is to build AI that amplifies human potential and empowers people around the world. We strive to deliver breakthroughs that advance science, education, productivity, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models. 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 HPC clusters powering MAI model training and inference. • Observability : 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. • Automation & Tooling : Build automation for deployments, incident response, scaling, and failover in 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 • 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. Preferred Qualifications • 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. Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $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: careers.microsoft.com 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 $160,200 - $261,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com 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.

Requirements

Must-haves

Microsoft AI
Our mission is to build AI that amplifies human potential and empowers people around the world. We strive to deliver breakthroughs that advance science, education, productivity, and global well-being.
Reliability & Availability : Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference.
Observability : 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.
Automation & Tooling : Build automation for deployments, incident response, scaling, and failover in 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.
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.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlkubernetesdockerci/cdazureawsgcppythongo

Platform: eightfold
Effort: medium

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