Applied AI Engineer, Codex Core Agent
About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users.
What this role actually needs.
About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. Responsibilities: - Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows. - Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases. - Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation. - Analyze failures in production and systematically improve robustness and reliability. - Build feedback loops and data systems that get better real-task data into evaluation and research. - Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the Applied AI Engineer, Codex Core Agent role.
- Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows.
- Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases.
- Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation.
- Analyze failures in production and systematically improve robustness and reliability.
- Build feedback loops and data systems that get better real-task data into evaluation and research.
- Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on.
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Source: jobs.ashbyhq.com · Source ID: 577e6673-0a4a-491b-9a0d-facbdd3bdf3c · Confidence: 97/100 · Last checked: May 7, 2026
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