Software Engineer, Productivity - Inference Runtime

About the Team We’re hiring a Developer Productivity engineer to support OpenAI’s Inference Runtime teams. These teams own the systems responsible for serving models reliably, efficiently, and safely across Codex, ChatGPT, API, and internal research workloads. We’re hiring a Developer Productivity Engineer to help scale the engineering systems, safeguards, and developer workflows that enable our teams to move quickly without compromising reliability or performance. This role sits at the intersection of developer experience, CI/CD infrastructure, release engineering, production readiness, and inference systems reliability. You’ll work on the tooling and operational foundations that support model launches, inference optimizations, cloud provider integrations, and large-scale deployments across a rapidly evolving inference stack. About the Role We’re looking for an autonomous, high-ownership engineer who cares deeply about making other engineers faster, safer, and more confident. A major focus of this role will be improving the tooling and infrastructure around deploy gates for inference engine images. These systems help ensure that every image released to production and research is correct, numerically sound, free of regressions, and performant across key metrics like time-to-first-token (TTFT) and time-between-tokens (TBT). You’ll help harden the systems that catch issues [... source excerpt omitted ...] ce optimizations to the world, onboard new infrastructure providers, and operate one of the largest and most performance-sensitive inference platforms in the world. In this role, you will: Improve systems that ensure inference engine releases are correct, performant, and regression-free by evolving tooling and infrastructure for deploy gate validation Bring rigor to release, validation, branching, and deployment processes across the inference stack Improve canary, async, and large-scale validation workflows for inference systems Harden CI, testing, and validation infrastructure so failures are actionable and trustworthy Reduce noisy or flaky failures caused by infrastructur [... source excerpt omitted ...] ity, or large-scale build and validation systems You are excited by high-impact infrastructure where small regressions in correctness, latency, or reliability meaningfully affect production systems You care about building systems engineers can trust, not just systems that technically function You have strong developer empathy and enjoy improving workflows, reducing friction, and making engineers more effective You demonstrate high ownership and proactively identify problems, drive improvements, and follow issues through resolution You are comfortable working in Python-heavy environments and debugging complex distributed systems You enjoy building automation that reduces manua