5-7 years of hands-on experience with post-training LLMs using SFT and RL.
Strong foundation in machine learning principles and distributed training.
Proficient in Python with a focus on production-quality code.
Experience in reward modeling or RL for language models, with a track record of implementing improvements.
Research publications in relevant fields are a plus, but not mandatory.
Responsibilities
Own and drive a research agenda for enhancing long-horizon legal reasoning capabilities.
Design and execute post-training experiments leveraging leading open-weight models.
Develop and analyze reward functions for semi-verifiable legal tasks.
Debug and optimize large-scale training processes, addressing issues like divergence and instability.
Implement rigorous evaluations to measure model improvements and inform deployment decisions.
Write, test, and debug code across the research stack.
Benefits
Opportunity to work on cutting-edge legal AI technology.
Collaborative environment with a small, high-performing team.
Direct impact on the development and deployment of models used by customers.
Access to resources for professional growth and research initiatives.
Full Job Description
Role Overview
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.
We recently post-trained our first model built for legal work, and we're now building the team to scale up our post-training efforts. You'll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You'll help set the research direction and carry ideas from experiments through training runs to models used by customers. You'll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work.
We're looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly.
Learn more about our work at harvey.ai/research.
What You'll Do
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.
What You Have
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.
We are hiring for this role across multiple levels.
Compensation
$230,000 - $340,000 USD
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