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  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.

We’re hiring research scientists , research engineers , and AI systems engineers to work on automating research s 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 ambig