Description
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.
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.
Key responsibilities include:
- Improving systems that ensure inference engine releases are correct, performant, and regression-free by evolving tooling and infrastructure for deploy gate validation
- Bringing rigor to release, validation, branching, and deployment processes across the inference stack
- Improving canary, async, and large-scale validation workflows for inference systems
- Hardening CI, testing, and validation infrastructure so failures are actionable and trustworthy
- Reducing noisy or flaky failures caused by infrastructure instability, GPU scheduling, or test environment issues
- Building automation for failure triage, ownership detection, debugging, and escalation
- Partnering closely with inference teams, research developer productivity, engine acceleration, and infrastructure teams to improve release quality and rollout safety
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
- You enjoy partnering closely with engineers to understand workflows, pain points, and operational challenges
- You are pragmatic, collaborative, and motivated by helping teams move faster with more confidence
- You are excited to learn about large-scale inference systems, even if you have not worked directly on inference before
Python experience is highly relevant, as much of the current deploy gate and validation infrastructure is Python-based. C++ experience is helpful, especially for working near inference engine code, CI build issues, or performance-sensitive systems, but it is not required.
Prior inference experience is not required.
The ideal candidate is someone with strong instincts around developer productivity, testing, release engineering, and automation who is excited to apply those skills in a deeply impactful inference environment. We’re looking for someone who is technically curious, comfortable navigating ambiguous, cross-functional operational problems, and is motivated to improve the reliability, safety, and developer experience of large-scale production infrastructure.