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 before they reach production, reduce noise from flaky or infrastructure-related test failures, and improve automation around triage, ownership, debugging, and escalation when failures occur. You’ll also work on improving observability, rollout safety, release automation, and developer self-service tooling across a rapidly evolving inference stack.

This is not generic internal tools work. The systems you build directly impact OpenAI’s ability to support new model launches, safely ship inference 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 infrastructure instability, GPU scheduling, or test environment issues

Build automation for failure triage, ownership detection, debugging, and escalation

Partner closely with inference teams, research developer productivity, engine acceleration, and infrastructure teams to improve release quality and rollout safety

Reduce developer friction in testing, debugging, and release workflows so engineers can move faster with confidence

You might thrive in this role if:

You have strong experience with CI/CD systems, testing infrastructure, release tooling, developer productivity, 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 manual triage, improves signal quality, and scales operational effectiveness

You are comfortable operating in ambiguous areas without a fully predefined roadmap

Yo…