软件工程师，生产力 - 推理运行时

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 str

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

• 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. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.