Software Engineer, Hardware Health

About the Role

On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale.

Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams.

Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally.

In this role, you will

• Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure.
• Build and evolve health checks that detect, remediate, and verify failures at scale.
• Ensure critical health checks execute with minimal latency to maximize workload uptime.
• Investigate hardware failures and system-level issues across large-scale compute environments.
• Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes.
• Build automation and tooling that enables global cluster management with minimal manual intervention.
• Partner with workload, reliability, and provider teams to integrate health signals into training and inference systems.

You Might Thrive in This Role If You Have

• 7+ years of industry experience in software or infrastructure engineering.
• Strong proficiency with Python and shell scripting.
• Experience building large-scale distributed systems or infrastructure platforms.
• Comfort digging into noisy operational data using SQL, PromQL, or similar tooling.
• Experience building reproducible analyses and operational tooling.
• Strong systems debugging and operational instincts with an ownership mindset.

Bonus If You Have

• Experience with low-level hardware systems and Linux tooling (e.g. PCIe, InfiniBand, RoCE, networking, power management, kernel performance tuning, FW/SW debugging).
• Experience operating or debugging large-scale GPU or accelerator clusters.
• Expertise in network operations, observability, or systems telemetry.
• Experience with automated remediation systems or fleet lifecycle management.
• Experience improving reliability, utilization, or workload uptime in distributed compute environments.