Research Scientist

The job: prove that games make models better.

You build expert agents that play games at a superhuman level, then distill that intelligence into general models. You isolate what game data and game-based RL do to model capability, and you make the transfer repeatable.

• Train expert game agents and distill their intelligence into general language models.
• Design dense reward signals and shape what gets learned. Our first transfer result came from sparse rewards and expensive rollouts. You help improve that process.
• Build training systems that target a benchmark: generate tasks that mirror its format and use the game engine as the verifier.
• Run controlled SFT (supervised fine-tuning) and RL experiments that measure how game environments change model performance.
• Design rewards and evaluations for multi-agent games: negotiation, long-horizon planning, cooperation, deception.
• Build public evaluations and benchmarks that show what games measure and math or code benchmarks miss.
• Publish papers, technical reports, and blog posts. Your results will help carry our research brand.
• Feed findings back into environment design with the engineering team.

• The profile we want most: you built a superhuman game agent, in the spirit of Alpha Star, Alpha Go, or OpenAI Five. Any company, any game, show us the agent and what it beat.
• Down for the mission. You love games and you view them as serious training grounds for intelligence.
• Hands-on RL post-training experience: PPO, GRPO, or RLVR.
• A public research record: papers, models, or benchmarks that other people used, cited, or built on.
• Designs small, fast experiments and pulls real conclusions from messy results.

• Remote-first with regular in-person gatherings. Must be in the US or Canada.