Research Engineer / Scientist, Reinforcement Learning (Remote)

A fast-growing applied AI company serving the legal and professional services sector is hiring a research engineer or scientist to lead post-training work on open-weight language models built for legal work. The team is small and high-bar, with each member owning a large part of the research and engineering. You will help set research direction and move ideas from experiments through training runs into models used by customers. Multiple seniority levels are being considered.

What you'll do

• Own a research agenda aimed at stronger long-horizon legal reasoning and agentic workflows.
• Design and run post-training experiments across supervised fine-tuning and reinforcement learning on leading open-weight models.
• Build reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models and expert preference data, and study their failure modes.
• Run and debug large-scale training, addressing divergence, reward hacking and instability.
• Create and use rigorous evaluations to measure gains and decide what ships.

What you need

• Hands-on experience post-training LLMs with SFT and RL, including preference optimization, with direct ownership of training runs and results.
• Solid ML fundamentals and a track record of getting distributed training runs working reliably.
• Strong Python engineering skills with production-quality code and end-to-end system ownership.
• Research experience in RL for language models or reward modeling, and the ability to move quickly from experiments to shipped improvements.
• Publications are welcome but not required.

How to apply

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