Software Engineer, Hardware Health

OpenAI•San Francisco, CA

About The Position

The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.