Researcher, Computer Use Agent Post-Training

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 OpenAIs 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 Computer Use you will teach models to operate computers. You will help train models that can navigate browsers and desktops use tools and applications reason through complex workflows collaborate with users and other agents and complete long-horizon tasks with reliability and judgment. This work sits at the intersection of frontier model training product behavior evaluation and systems engineering and will directly shape the computer-use capabilities shipped in OpenAIs next generation of agents. Currently our models are the best in the world at this behavior!

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 might

• Design and run experiments that improve agentic model behavior for complex computer use including desktop and browser.

• 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.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core and to achieve our mission we must encompass and value the many different perspectives voices and experiences that form the full spectrum of humanity.

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

For additional information please see OpenAIs Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws including the San Francisco Fair Chance Ordinance the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct adverse and negative relationship with the following job duties potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary confidential and non-public addition job duties require access to secure and protected information technology systems and related data security obligations.

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Required Experience:

Unclear Seniority