[Hiring] Research Engineer / Scientist, Reinforcement Learning @Harvey

Role Description

As a Member of Technical Staff, Modeling in Harvey Research, you’ll own how we adapt and improve leading open-weight models for legal work.

• 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.

Qualifications

• 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.

Requirements

• We are hiring for this role across multiple levels.

Compensation

• $230,000 - $450,000 USD

Equal Opportunity Statement

Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing [email protected].