Agent Post-Training, Computer Use Research

Details

About the Team

The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We train models behind 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. Work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. The team builds data, environments, graders, training methods, and feedback loops that shape OpenAI's next agents and carries those capabilities through major training runs into products.

Responsibilities

• Design and run experiments that improve agentic model behavior for complex computer use (desktop and browser).
• Own end-to-end improvements to the post-training stack, including reinforcement learning (RL), data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.
• Build evals and environments that expose model failures and convert failures into training data, product fixes, or research directions.
• Partner with Codex and ChatGPT product teams to translate product signal into model improvements.
• Work on early-training and alignment interventions such as 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 training and launch infrastructure: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Lead cross-functional projects touching model training, product infrastructure, and production agent harness (e.g., multi-agent systems, 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 the ability to learn quickly across unfamiliar areas.
• Hands-on experience with LLMs, reinforcement learning (RL), RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.
• Experience designing and running experiments end-to-end: hypothesis definition, pipeline construction, model runs, analysis, and iteration.
• Ability to work cross-functionally across research, product, infrastructure, data, evals, and safety teams and communicate clearly with each group.
• Comfortable building load-bearing systems and processes when needed (pipelines, grading, diagnostics, reproducibility, observability).
• Interest in product impact and model behavior (reliability, honesty, usefulness, and taste) rather than only benchmark improvements.

Benefits

• Estimated base salary range: $295,000 – $445,000 (base pay varies by market location, knowledge, skills, and experience).
• Total compensation may include equity and performance-related bonuses for eligible employees.
• Medical, dental, and vision insurance with employer contributions to Health Savings Accounts.
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental leave, paid medical and caregiver leave; flexible PTO for exempt employees and up to 15 days annually for non-exempt employees.
• 13+ paid company holidays and coordinated office closures.
• 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 (charitable donation matching, wellness stipends) as applicable.

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, diverse perspectives, and equitable hiring practices. Background checks are administered in accordance with applicable law. OpenAI is an equal opportunity employer and provides reasonable accommodations to applicants with disabilities.