The opportunity
The Codex Research team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other…
What you'll do
Design and run experiments that improve agentic model behavior across coding,: tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning.
Own end-to-end improvements to the post-training stack, including RL, data: pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.
Build evals and environments that expose the next set of model failures, then: turn those failures into training data, product fixes, or new research directions.
Partner with Codex, API/platform, ChatGPT, and general-agent product teams to: understand what users need and translate product signal into model improvements.
Work on early-training and alignment interventions, including data mixtures,: objectives, synthetic data, and eval loops that shape downstream agent behavior.
Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.
What they're looking for
- Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
- Take on cross-functional projects that touch model training, product: infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.
- Debug hard failures in shipped or near-shipped models and turn messy: qualitative behavior into concrete hypotheses, experiments, and fixes.
- Have strong technical fundamentals in machine learning, software engineering,: systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.
