Research Engineer / Scientist, Reinforcement Learning

📋 Description

• Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and
• Design and run post-training experiments across SFT and RL, building on leading open-weight models.
• Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned
• Run and debug training at scale, addressing divergence, reward hacking, and instability.
• Build and use rigorous evals to track improvements and decide what ships.
• Design, implement, test, and debug code across our research stack.

🎯 Requirements

• Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and
• Deep ML fundamentals and experience getting distributed training runs working reliably.
• Strong engineering skills in Python, with production-quality code and end-to-end system ownership.
• Research experience in RL for language models or reward modeling, with the ability to move quickly