Описание

Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows.
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 preference models, and expert preference data, and study where they break.
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.

Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results.
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 from experiments to shipped improvements.
Publications welcome, not required.

Research Engineer / Scientist, Reinforcement Learning