Research Engineer, Research Scientist, AI Systems Engineer

• 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 models and OpenAI’s research infrastructure
• Develop research agents, experiment-orchestration systems, and sandboxed runtimes supporting real research workflows
• Create metrics and economic models to understand RSI’s effects on research productivity, model capabilities, and safety of internal deployments
• Work across research, engineering, product, and infrastructure to automate research workflows and improve research productivity
• Contribute across the full lifecycle of model training, evaluation, and deployment

• Research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems
• Strong generalist ability to move between open-ended research and practical implementation
• Ability to turn ambiguous problems into clear results
• Effective collaboration across systems, data, model training, evaluations, and research teams
• Experience building and maintaining data pipelines, tooling, and infrastructure for emerging AI capabilities
• Comfort working on problems without clear definitions or established playbooks
• Rigorous thinking about scientific quality, research taste, safety, privacy, reliability, performance, and scale
• Excitement about using increasingly capable AI systems to accelerate meaningful research

Demonstrates expertise in LLM Training, Model Evaluations, and Research Infrastructure, with a strong ability to automate research workflows and enhance research productivity through effective collaboration and rigorous scientific thinking.

• LLM Training
• Model Evaluations
• Agent Systems
• Data Pipeline Development
• Research Infrastructure

• Model Training
• Experiment Design
• Synthetic Data Generation
• Research Workflow Automation
• Evaluation Metrics Development

• Effective Collaboration
• Rigorous Thinking
• Problem-Solving

• Large-Scale Distributed Systems
• AI Capabilities
• Research Productivity
• Model Capabilities
• Safety and Privacy

• Research Infrastructure
• Experiment-Orchestration Systems
• Sandboxed Runtimes