About the Team The Codex team is responsible for building state-of-the-art AI systems that can write code, reason about software, and act as intelligent agents for developers and non-developers alike. Our mission is to push the frontier of code generation and agentic reasoning, and deploy these capabilities in real-world products such as ChatGPT and the API, as well as in next-generation tools specifically designed for agentic coding. We operate across research, engineering, product, and infrastructure—owning the full lifecycle of experimentation, deployment, and iteration on novel coding capabilities. About the Role As a Research Engineer or Research Scientist on the Codex team, you will advance the capabilities of AI coding models through novel research, hands-on experimentation, and scalable implementation. You’ll work closely with world-class researchers and engineers to develop and deploy systems that help millions of users write better code, faster. We’re looking for people who combine deep curiosity, strong technical fundamentals, and a bias toward impact. You might have a background in ML research, systems, or software engineering, but above all, you care about pushing the state of the art of coding models and putting them in the hands of real users. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Design and run experiments to improve code generation and agentic behavior in Codex models.

Develop research insights into model training, alignment, and evaluation.

Work across the stack to prototype new capabilities, debug complex issues, and ship improvements to production.

You might thrive in this role if you: Are excited to explore and push the boundaries of large language models, especially in the domain of software reasoning and code generation.

Have strong software engineering skills and enjoy quickly turning ideas into working prototypes.

Bring creativity and rigor to open-ended research problems and enjoy working in highly iterative environments.