Researcher, Artifacts - Agent Post-Training

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, with ability to learn quickly across new areas
• 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
• Excitement for open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution
• Focus on product impact and model behavior, with opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with
• Ability to 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
• Comfort working across research, product, infrastructure, data, evals, and safety boundaries, with clear communication skills
• Willingness to build load-bearing systems and processes when needed, even if the work is not glamorous
• Drive to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users

Nice-to-Haves

• Prior background in consulting, finance, marketing, operations, or data science