Performance engineer • oakland ca
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The average salary range is between $ 86,320 and $ 131,900 year , with the average salary hovering around $ 94,907 year .
The average salary range is between $ 106,478 and $ 185,900 year , with the average salary hovering around $ 130,004 year .
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 Performance & Systems Engineer on the Codex team, you will be responsible for whole-system optimization across a complex, evolving stack. Codex spans LLM inference, cloud orchestration, agentic work management, and multiple product surfaces. 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 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, 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.
You might thrive in this role if you:
Have 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.
