Research Engineer / Scientist, Reinforcement Learn

Responsibilities

• Own and pursue research to improve model capability on long-horizon legal reasoning and agentic workflows.
• Design and run post-training experiments using supervised fine-tuning and reinforcement learning on leading open-weight models.
• Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data.
• Run and debug large-scale training while addressing divergence, reward hacking, and instability.
• Build and use rigorous evaluations to track improvements and determine what ships.
• Design, implement, test, and debug code across the research stack.

Requirements

• Hands-on experience post-training large language models with supervised fine-tuning, reinforcement learning, and preference optimization.
• Direct ownership of training runs and results.
• Deep machine learning fundamentals and experience getting distributed training runs working reliably.
• Strong Python engineering skills, including production-quality code and end-to-end system ownership.
• Research experience in reinforcement learning for language models or reward modeling.
• Ability to move quickly from experiments to shipped improvements.
• Publications are welcome but not required.