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.
What You'll Do 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.
What We Look For The profile we want most: you built a superhuman game agent, in the spirit of AlphaStar, AlphaGo, 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, quick experiments and pulls real conclusions from messy results.
Comp and Location Remote-first with regular in-person gatherings. Must be in the US or Canada.

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📌 RESEARCH SCIENTIST (Quebec City)
🏢 Good Start Labs
📍 Quebec City