About the job
OpenAI is looking for exceptional researchers to join the Post-Training Frontiers team, which is responsible for post-training the agentic models we ship across Codex, the API, ChatGPT Thinking, and ChatGPT Pro. The Post-Training Frontiers team sets up the pipeline for deciding which integrations can go into the post-training run, develops its own horizontal improvements to the model, and trains the final model.
Responsibilities
- Own end-to-end research and engineering projects that improve the final post-training of OpenAI’s agentic models.
- Decide, together with partner teams, which integrations are ready for inclusion in major model runs.
- Develop horizontal model improvements across factuality, instruction following, tool/function calling, multi-agent behavior, reasoning-effort calibration, and other broad capabilities.
- Build and improve training, evaluation, grading, and data infrastructure for large-scale RL/post-training runs.
- Create evals and diagnostics that help us understand whether a model is ready to ship.
- Improve the feedback loop from real product usage into post-training, including better ways to learn from implicit user feedback.
- Collaborate closely with Codex, API, ChatGPT, product, training, and other post-training teams to make frontier models more useful, reliable, and agentic.
Qualifications
Minimum
No minimum qualifications listed.
Preferred
- Experience with large-scale model training or RL systems.
- Experience building evals, graders, reward models, or data pipelines for LLM training.
- Experience with coding agents, tool-using agents, browser/computer-use agents, function calling, or multi-agent systems.
- Background in quant, systems, infra, or other environments where you built reliable machinery for high-stakes experimentation.
- Evidence of strong product taste, especially around writing, design, code generation, or agent workflows.