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 this API & power-users team you will improve the capabilities reliability and product fit of OpenAIs agentic models for power users and API developers. You might design evals from real developer workflows build training environments around production-like tool use turn qualitative model failures into training data evals or post-training interventions or drive a behavior improvement from discovery through post-training integration and launch.

This role is intentionally broad. The strongest candidates are comfortable turning ambiguous model behavior problems into concrete progress whether that means improving tool use planning instruction following recovery from mistakes or how models behave in API-based workflows. You should be excited to work across research engineering data evals and product to make models better at acting in real workflows.

You will work closely with researchers engineers API/product teams Codex infrastructure and safety/alignment partners to decide which behaviors matter how to measure them how to train them and when they are ready for major model runs. This is a high-agency role for people who want their work to show up directly in frontier models used by expert users and developers.

In this role you might

• Design and run experiments that improve model behavior in API and power-user workflows: function calling tool use coding planning long-horizon execution factuality instruction following error recovery and calibrated reasoning.

• Build evals graders and environments from real developer and power-user workflows then turn observed failures into training data model-behavior hypotheses and shipped improvements.

• Partner with API and power-users to identify high-leverage behavior gaps and convert product signals into post-training interventions.

• Improve how models behave when composed into systems: using tools reliably respecting developer intent handling partial failures asking for clarification when appropriate and maintaining coherence across multi-step tasks.

• Own end-to-end model behavior projects from qualitative failure analysis through data generation training experiments eval design integration into major runs and launch readiness.

• Develop feedback loops that use power-user traces API usage patterns and production-like environments to discover the next frontier of agentic model failures and gaps.

• Help decide which agentic capabilities behavioral fixes and partner-team integrations are ready for inclusion in major model runs.

• Debug hard failures in shipped or near-shipped models by moving between traces evals training data model outputs and product context.

• Work on early-training and alignment interventions including data mixtures objectives synthetic data and eval loops that shape downstream agent behavior.

• 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.

You might thrive in this role if you

• Have strong technical fundamentals in ML software engineering systems statistics or applied research and can quickly learn across unfamiliar parts of the stack.

• Have hands-on experience with LLMs post-training RL/RLHF/RLAIF evals graders synthetic data coding agents tool-using agents API products or production ML systems.

• Have strong taste for model behavior: you can look at a transcript trace eval failure or API interaction and form concrete hypotheses about what the model needs to learn.

• Are excited by ambiguous capability problems where the signal is noisy the failures are qualitative and the solution may involve data training evals product changes or all of the above.

• Deeply care about developer and expert-user experience especially how models behave when embedded in real user workflows API products and agent harnesses..

• 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.

To notify OpenAI that you believe this job posting is non-compliant please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities and requests can be made via this link.

OpenAI Global Applicant Privacy Policy

At OpenAI we believe artificial intelligence has the potential to help people solve immense global challenges and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Required Experience:

Unclear Seniority