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engineering

Posted May 1

Performance & Systems Engineer, Codex

at openai

San Francisco, United StatesHybrid

Responsibilities

  • you will: - Hunt down and address inefficiencies across the Codex system stack, from agent behavior to LLM inference to container orchestration, and beyond. - Build tooling to measure, profile, and optimize system performance at scale. - Collaborate with researchers and engineers to land high-ROI changes that improve latency and cost.

Requirements

  • 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.
  • Codex spans LLM inference, cloud orchestration, agentic work management, and multiple product surfaces.
  • experience of millions of 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,
  • experience operating across both ML systems and cloud infrastructure. - Enjoy diving into messy, ambiguous problems and emerging with clear wins. - Think holistically about performance, balancing speed, cost, and user experience.
  • About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence

Additional details

  • 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 Performance & Systems Engineer on the Codex team, you will be responsible for whole-system optimization across a complex, evolving stack.
  • Your job will be to identify and land high-leverage changes—across infrastructure, modeling, and product layers—that make Codex agents significantly faster and cheaper to serve.
  • We’re looking for generalists who thrive in ambiguity and love chasing performance bottlenecks to ground.
  • This is a high-ownership role where your work will directly improve the

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