Software Engineer, Productivity - Inference Runtime

OpenAI is hiring a Developer Productivity engineer to support its Inference Runtime teams. These teams own the systems responsible for serving models reliably, efficiently, and safely across Codex, ChatGPT, API, and internal research workloads. This role sits at the intersection of developer experience, CI/CD infrastructure, release engineering, production readiness, and inference systems reliability. The engineer will work on tooling and operational foundations that support model launches, inference optimizations, cloud provider integrations, and large-scale deployments across a rapidly evolving inference stack. A major focus will be improving tooling and infrastructure around deploy gates for inference engine images to ensure 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).

Responsibilities

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

Requirements

Strong experience with CI/CD systems, testing infrastructure, release tooling, developer productivity, or large-scale build and validation systems; Excited by high-impact infrastructure where small regressions in correctness, latency, or reliability meaningfully affect production systems; Care about building systems engineers can trust, not just systems that technically function; Strong developer empathy and enjoy improving workflows, reducing friction, and making engineers more effective; High ownership and proactively identify problems, drive improvements, and follow issues through resolution; Comfortable working in Python-heavy environments and debugging complex distributed systems; Enjoy building automation that reduces manual triage, improves signal quality, and scales operational effectiveness; Comfortable operating in ambiguous areas without a fully predefined roadmap; Enjoy partnering closely with engineers to understand workflows, pain points, and operational challenges; Pragmatic, collaborative, and motivated by helping teams move faster with more confidence; Excited to learn about large-scale inference systems, even if not worked directly on inference before; Python experience is highly relevant

Nice to have

C++ experience is helpful