Agent Post-Training, Connectors Research

Responsibilities

• Design and run experiments that improve agentic model behavior for complex software and plugins.
• 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 and ChatGPT 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.
• 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.

Requirements

• 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.
• Hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.
• Excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.
• Care about product impact and model behavior, not just benchmark movement. Have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.
• Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.
• Comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.
• Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
• Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.