Agent Post-Training, Context Research
About The Position
The Agent Post-Training team at OpenAI is responsible for training the models behind frontier agents, including those used in Codex, ChatGPT, and other products. These agents are designed to be persistent, proactive, and capable of operating computers, collaborating with people and other agents, and expanding human potential. The team defines future agent capabilities, develops the training signals, and conducts experiments to realize these capabilities. Their work encompasses areas such as coding, tool use, computer interaction, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. This team is central to the creation of new model capabilities, building the data, environments, graders, training methods, and feedback loops that shape OpenAI's next generation of agents and bring them to market. As a Context Researcher on the Agent Post-Training team, the primary focus will be on scaling the compute spent on context, which is considered a key enabler for AGI. This role involves working within OpenAI's frontier training stack to advance the next paradigm of model training, with a direct product interface for iterative deployment (Codex Chronicle). The researcher will collaborate with various teams, including researchers, engineers, product teams, infrastructure teams, and safety/alignment partners, to determine the content of major model runs, evaluate their success, and implement improvements in products used by a wide audience. This is a high-agency position for individuals who want their contributions to directly impact frontier models.
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. 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.
- 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.
Responsibilities
- 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.
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- Life insurance
- Disability insurance
- 401k
- Flexible scheduling
- Professional development
- Learning development program
- Continued education
- Tuition reimbursement
- Employee discount programs
- Wellness programs
- On-site amenities
- Pet friendly
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
