Researcher, Artifacts - Agent Post-Training

Details

About the Team

The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We train the models behind agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.

We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.

Our team builds the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, and carries those capabilities through major training runs into products used by real people.

About the Role

As a member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This requires owning improvements across the post-training stack, including reinforcement learning, data pipelines, graders, reward signals, evaluations, and behavioral analysis.

You will collaborate 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. This is a high-agency role whose work lands directly in frontier models.

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 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.
• Decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.
• Improve machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Lead cross-functional projects touching model training, product infrastructure, and the production agent harness (e.g., multi-agent systems or training against production-like environments).
• Debug hard failures in shipped or near-shipped models and turn 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 unfamiliar 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.
• Ability to work on open-ended problems where the path is unclear and the signal is noisy, combining research taste and engineering execution.
• Focus on product impact and model behavior, not just benchmark performance; strong opinions about usefulness, reliability, honesty, and usability of agents.
• Ability to move from vague behavioral problems to concrete experiments: define hypotheses, build pipelines, run models, analyze results, and decide next steps.
• Comfort working across research, product, infrastructure, data, evals, and safety boundaries and communicating clearly with each group.
• Willingness to build load-bearing systems and processes when needed.
• Interest in training and shipping models that make agents useful for developers, enterprises, researchers, and everyday users.
• Some prior background in consulting, finance, marketing, operations, or data science is a plus.

Compensation

• Compensation Range: $250K - $380K USD

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. The company emphasizes safety and inclusive perspectives and is an equal opportunity employer. Background checks will be administered in accordance with applicable law. OpenAI is committed to providing reasonable accommodations to applicants with disabilities.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays and multiple coordinated office closures, plus paid sick or safe time
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend
• Daily meals in offices and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits (e.g., charitable donation matching and wellness stipends)