Member of Technical Staff, Modeling

Harvey • New York, NY

12d • $230,000 - $340,000

About The Position

As a Member of Technical Staff, Modeling in Harvey Research, you will own how Harvey adapts and improves leading open-weight models for legal work. Harvey has post-trained its first model built for legal work and is growing its team to scale post-training efforts. You will own the post-training recipe across supervised fine-tuning (SFT) and reinforcement learning (RL), including reward design and verification for challenging, semi-verifiable legal tasks.

You will help set research direction and carry ideas from experiments through training runs to models used by customers. You will join a small, high-bar team of hands-on engineers and researchers, with each person owning a substantial part of the work. Harvey seeks research engineers and scientists with experience running training at scale, expertise in RL for language models, and an interest in delivering work to users quickly.

Requirements

• Hands-on experience post-training LLMs with SFT and RL, including preference optimization, with direct ownership of training runs and results.
• Deep machine-learning fundamentals and experience getting distributed training runs working reliably.
• Strong Python engineering skills, including 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.

Nice To Haves

• Publications are welcome but not required.

Responsibilities

• 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 their failure modes.
• Run and debug training at scale, addressing divergence, reward hacking, and instability.
• Build and use rigorous evaluations to track improvements and decide what ships.
• Design, implement, test, and debug code across the research stack.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed