Original listing text, shown exactly as published by the company.
About the Role
We believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.
In this role, you will
- Design and run experiments that improve scaling of compute on context.
- 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.
You might thrive in this role if you
- 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.
- Have 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.
- Are 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. You 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.
- Are 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.
Compensation Range: $250K - $380K USD
