AI Research Scientist

What you'll do

• Design and train reinforcement-learning systems for scientific hypothesis generation and evaluation.
• Develop process reward models, verifiers, and oversight methods for complex scientific reasoning.
• Build training pipelines, run large-scale experiments, and contribute to domain-specific benchmarks and evaluations.

What they ask for

• Strong machine-learning fundamentals and hands-on experience training large models; LLM experience is preferred but not required.
• Demonstrated reinforcement-learning experience, including experience from fields such as robotics, game-playing, or scientific discovery.
• Ability to work across research and engineering, including writing papers and debugging distributed training jobs.
• Familiarity with at least some of reward modeling, RLHF/RLAIF pipelines, search and planning methods, or AI alignment techniques.

Nice to have

• Publication record.
• For senior roles: independently driving high-impact research, mentoring researchers, influencing technical strategy, and deep expertise in a core technical area.

Tools

RLHF, RLAIF

AI focus

Research and engineering for AI reasoning in scientific tasks, including reinforcement learning, process supervision, scalable oversight, and evaluation.