Agent Post-Training Research

About The Team

The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our 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 is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.

About The Role

As a member of Agent Post-Training, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. 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.

Responsibilities

• Design and run experiments that improve agentic model behavior across coding, tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning.
• Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.
• Create 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, API/platform, 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.
• Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.

Requirements

You should 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. 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 is preferred. You 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. You 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. You 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. You are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group. You like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous. You want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.

Qualifications

Qualified candidates will have a Bachelor's degree in Computer Science, Engineering, Statistics, or a related field, and at least 3 years of relevant work experience. A Master's degree or PhD in a relevant field is preferred. Experience with large-scale machine learning systems, reinforcement learning, and evaluation methodologies is highly desirable. Strong communication skills and the ability to work effectively in a multidisciplinary team are essential.

Skills

Strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field. 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. Care about product impact and model behavior, not just benchmark movement. 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. Comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and clear communication skills. Desire to build load-bearing systems and processes when needed, even if the work is not glamorous. Commitment to training and shipping models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.

Benefits

OpenAI offers a competitive compensation package, including a range of $295K - $445K, depending on experience and qualifications. We also provide comprehensive benefits, including health insurance, retirement plans, paid time off, and opportunities for professional development and growth.

Pay

$295K - $445K

Schedule

Full-time, remote position available.

Equal Opportunity Employer

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or any other applicable legally protected characteristic.