Applied Scientist, Reinforcement Learning (Mid, Senior, Staff)

Hippocratic AI; Menlo Park (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Hippocratic AI is seeking experts to own the Reinforcement Learning (RL) and On-Policy Distillation (OPD) post-training pipeline for healthcare LLMs. You will improve clinical reasoning, safety, and alignment, deploying models to interact with millions of patients across diverse clinical use cases.

The role requires 5+ years in NLP, LLM training, or RL, with 2+ years in RL for LLM post-training, and hands-on experience with large-scale multi-node LLMs in a Palo Alto setting.

Qualifications

• Requires MS/PhD in CS or related field and strong background in NLP, RL, and ML.
• Proficiency in Python and PyTorch with experience in large-scale LLM training.
• Experience with RLHF, RLVR, and post-training methods for LLMs.

Responsibilities

• Design RL and OPD post-training methods (RLHF, RLVR, OPD) for healthcare LLMs.
• Build and evaluate reward models, verifiers, and LLM-as-judge pipelines.
• Develop conversational AI environments and simulations for RL training with synthetic data.
• Automate post-training loops with agents and automate experiments.
• Run rigorous experiments to understand drivers of post-training gains.
• Collaborate with research, engineering, and clinical teams.

Skills

Python

PyTorch

NLP

RL training

RLHF/RLVR

Education

MS or PhD in CS or relevant field

Tools

PyTorch (distributed)

Job description

Hippocratic AI is the leading generative AI company in healthcare. We have the only system that can have safe, autonomous, clinical conversations with patients. We have trained our own LLMs as part of our Polaris constellation, resulting in a system with over 99.9% accuracy.

Hippocratic AI is the leading generative AI company in healthcare. We have the only system that can have safe, autonomous, clinical conversations with patients. We have trained our own LLMs as part of our Polaris constellation, resulting in a system with over 99.9% accuracy.

Why Join Our Team

Reinvent healthcare with AI that puts safety first. We're building the world's first healthcare-only, safety-focused LLM - a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.

Work with the people shaping the future. Hippocratic AI was co-founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.

Backed by the world's leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children's, WellSpan Health, John Doerr, Rick Klausner, and others.

Build alongside the best in healthcare and AI. Join experts who've spent their careers improving care, advancing science, and building world-changing technologies - ensuring our platform is powerful, trusted, and truly transformative.

Location Requirement

We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Palo Alto office five days a week, unless otherwise specified.

About The Role

LLM post-training is where raw capability becomes reliable, safe behavior - and in healthcare, the stakes are as high as they get. You'll own the Reinforcement Learning (RL) and On-Policy Distillation (OPD) post-training pipeline end to end, to improve our models' clinical reasoning, safety, and alignment. Your models will be deployed to interact with millions of patients across diverse clinical use cases.

What You'll Do

• Design RL and OPD post-training methods (RLHF, RLVR, OPD, etc.)
• Build and evaluate reward models, verifiers, and LLM-as-judge pipelines
• Develop conversational AI environments and simulations for healthcare RL training with synthetic data
• Automate post-training loops with agents (auto-research)
• Run rigorous experiments to understand what drives post-training gains
• Collaborate with research, engineering, and clinical teams

What You Bring

• MS or PhD in CS or relevant field
• 5+ years or experience in NLP, LLM training, or RL
• 2+ years experience in RL for LLM post-training
• Experience with large-scale (50B+ parameter and multi-node) LLM training
• Strong Python and PyTorch coding skills
• Experience with RLHF, RLVR, LLM-as-judge or similar methods for LLM post-training

Nice-to-Have

• Publications at top venues (NeurIPS, ICML, ICLR, ACL, EMNLP)
• Healthcare domain experience