Research Engineer / Scientist, Reinforcement Learning
At a glance
Mid-level AI Research role at Harvey. Remote, United States · full-time · $230,000–$450,000 base.
remoteroles summary, based on the employer's posting.
What you'll do
- Set research priorities for improving long-range legal reasoning and agent workflows in language models
- Run supervised fine-tuning and reinforcement-learning experiments on leading open-weight models
- Create reward measures and checkers for legal tasks, then investigate failures in rubrics, preference models and expert feedback
- Troubleshoot large training runs when they become unstable, diverge or exploit their reward signals
- Build evaluation methods to measure gains and guide release decisions, while implementing and debugging research code
What you bring
- You have personally post-trained language models with supervised fine-tuning and reinforcement learning, including preference optimization
- You have owned training runs and their results, and can make distributed training reliable using strong machine-learning fundamentals
- You write production-quality Python and can take an entire system from development through operation
- Your research applies reinforcement learning to language models or reward modeling, with a record of moving experiments into shipped improvements
Who this fits
This suits a hands-on research engineer or scientist who wants to carry experiments into models used by customers. The work combines research ownership with implementation, training and evaluation. The role is remote in the United States.
From the employer
Why Harvey
At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.
This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.
Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.
At Harvey, the future of professional services is being written today — and we’re just getting started.
Role Overview
In this role, 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 - $450,000 USD
Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices here.
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 accommodations@harvey.ai
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