Software Engineer, Compute Infra HPC

Microsoft AI•Redmond, WA

5d•$119,800 - $304,200•Hybrid

About The Position

Microsoft AI is looking for experienced Member of Technical Staff, Software Engineers - Compute Infra/HPC to build the compute infra software that brings frontier AI compute online and keeps it healthy at global scale. You will work across cluster bring-up and automation, fleet and node lifecycle and health, and automated operations at global scale. The systems you build will transform newly delivered compute capacity into secure, observable, schedulable clusters and continuously recover usable capacity when hardware or software fails. This is a hands-on software engineering role at the boundary of distributed systems and AI supercomputing. You will design control planes, APIs, workflow engines, state models, and hardware-aware automation for some of the largest GPU systems in the world. Rather than relying on manual intervention, you will turn recurring operational workflows and failure modes into durable platform capabilities that improve time to production, fleet availability, and researcher velocity. 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.

Requirements

Bachelor's Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field AND 4+ years of software engineering experience building production distributed systems, infrastructure platforms, or control-plane services OR equivalent experience
4+ years of experience designing and implementing scalable software for cloud, datacenter, cluster, or fleet infrastructure using one or more general-purpose languages such as Go, Rust, C++, C#, Java, or Python
Experience with Kubernetes or comparable cluster-orchestration systems, Linux systems, public-cloud infrastructure, and infrastructure-as-code or declarative configuration
Demonstrated ability to debug complex behavior across service, control-plane, operating-system, networking, and hardware boundaries and turn findings into robust software improvements
Experience leading projects across multiple teams and communicating technical tradeoffs clearly to engineering and non-engineering stakeholders

Nice To Haves

Master's Degree in Computer Science or a related technical field AND 6+ years of software engineering experience building distributed systems or infrastructure platforms OR equivalent experience
Experience building or managing compute infrastructure at hyperscale, such as fleets with thousands of nodes, multiple clusters, heterogeneous accelerator generations, or capacity spanning multiple cloud and datacenter providers
Experience with cluster and node lifecycle systems, including bare-metal provisioning, cluster bring-up, health checking, certification, maintenance, repair automation, capacity management, or decommissioning
Depth in Kubernetes internals or related orchestration technologies, including controllers and operators, scheduler, autoscaler, kubelet, Cluster API, container runtimes, or workflow engines
Experience building cloud foundations, including virtual networking, CNI, private connectivity, DNS, workload identity, RBAC, secrets, image and artifact distribution, and Terraform or equivalent infrastructure-as-code systems
Experience designing health, diagnostics, and remediation platforms using event-driven architectures, durable workflows, state machines, telemetry pipelines, automated decisioning, and safe recovery mechanisms
Low-level systems experience with Linux kernels, device drivers, firmware, BMC or Redfish, secure boot or attestation, virtualization, or hardware-health agents
Experience with GPU or accelerator systems and high-performance interconnects such as InfiniBand, RoCE or RDMA, NVLink, NCCL or collective communication, as well as high-performance storage and data movement
Experience owning production reliability and incident response while systematically eliminating recurring toil through software and automation
Dedication to writing clean, testable, maintainable, secure, and well-documented code, with strong engineering judgment around correctness, scalability, operability, and long-term platform evolution
Ability to create alignment across senior stakeholders, mentor engineers, and drive complex multi-quarter technical initiatives from architecture through production adoption
Passion for learning rapidly evolving infrastructure technologies and for making enormous compute systems more reliable, efficient, understandable, and easy to use

Responsibilities

Design and build distributed services, control planes, and platform APIs for the end-to-end lifecycle of AI compute clusters, from capacity ingestion and provisioning through certification, upgrades, maintenance, repair, and decommissioning.
Build foundational primitives that make new capacity enablement repeatable and secure by default, including cluster bootstrap, Kubernetes control planes, networking, identity and access, image distribution, secrets, infrastructure as code, and cluster metadata across Azure and partner clouds.
Own software systems that reduce cluster bring-up time by automating rack and node qualification, topology validation, scale testing, and the transition from newly delivered hardware to healthy, schedulable fleet.
Develop the node and fleet health platform that combines telemetry, health signals, diagnostics, and lifecycle state to detect, isolate, and remediate unhealthy hardware, hosts, networks, and storage with minimal human intervention.
Build policy-driven and agent-assisted automation for certification, maintenance, safe rollout or rollback of software and platform components.
Use production evidence, benchmarks, workload profiles, and fault injection to improve system architecture, efficiency, failure detection, recovery time, and the quality of health and readiness decisions.
Translate incidents and repeated operational work into durable software, stronger abstractions, automated guardrails, and clear ownership boundaries rather than one-off manual procedures.
Partner closely with researchers, hardware health, networking, storage, security, and datacenter teams to shape the long-term architecture of Microsoft AI compute infrastructure.
Provide technical leadership through design reviews, architecture decisions, code quality, mentoring, and execution of complex cross-team initiatives.
Embody our culture and values.