Research Engineer / Research Scientist / AI Systems Engineer, RSI

About the Team

The Recursive Self-Improvement (RSI) team works across research, engineering, product, and infrastructure to build AI systems that accelerate and ultimately conduct high-quality research at OpenAI. We work to automate real research workflows and improve research productivity by building systems and feedback loops, designing evaluations, and training models to develop missing capabilities.

Our work spans the full lifecycle of model training, evaluation, and deployment to help researchers move faster and tackle increasingly ambitious problems.

About the Role

We’re hiring research scientists, research engineers, and AI systems engineers to work on automating research at OpenAI.

This role is based in San Francisco, CA.

In this role, you will

• Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution.

• Turn real research workflows and model failures into data and evaluation flywheels.

• Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training.

• Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure.

• Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows.

• Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments.

This is a high-ownership role for researchers and engineers who thrive in ambiguity, move fluidly between research and implementation, and turn emerging opportunities into rigorous, reliable, scalable results.

You might thrive in this role if you

• Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems.

• Are a strong generalist who can move between open-ended research and practical implementation, turning ambiguous problems into clear results.

• Collaborate effectively across the full stack, including systems, data, model training, evaluations, and other research teams.

• Are comfortable building and maintaining the data pipelines, tooling, and infrastructure needed to support emerging AI capabilities.

• Are comfortable working on problems without clear definitions or established playbooks.

• Think rigorously about scientific quality, research taste, safety, privacy, reliability, performance, and scale.

• Are excited about using increasingly capable AI systems to accelerate meaningful research.

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 OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf.

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 unincorp

Responsibilities

• Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution.
• Turn real research workflows and model failures into data and evaluation flywheels.
• Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training.
• Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure.
• Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows.
• Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments.

Requirements

• Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems.
• Are a strong generalist who can move between open-ended research and practical implementation, turning ambiguous problems into clear results.
• Collaborate effectively across the full stack, including systems, data, model training, evaluations, and other research teams.
• Are comfortable building and maintaining the data pipelines, tooling, and infrastructure needed to support emerging AI capabilities.
• Are comfortable working on problems without clear definitions or established playbooks.
• Think rigorously about scientific quality, research taste, safety, privacy, reliability, performance, and scale.
• Are excited about using increasingly capable AI systems to accelerate meaningful research.