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Hippocratic AI

Licenses safety-focused healthcare LLMs to institutions

Senior Applied Scientist - Reinforcement Learning

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeMenlo Park, CA, USANo salary listed

About the Job

Requirements

  • A master's or doctoral degree in Computer Science or a relevant field is required.
  • At least 3 years of experience in natural language processing, large language model training, or reinforcement learning is required.
  • At least 1 year of experience in reinforcement learning for large language model post-training is required.
  • Experience with large-scale training of 50-billion-plus-parameter, multi-node large language models is required.
  • Strong Python and PyTorch coding skills are required.
  • Experience with reinforcement learning from human feedback, reinforcement learning with verifiable rewards, LLM-as-judge, or similar methods for large language model post-training is required.

Responsibilities

  • Design reinforcement learning and on-policy distillation post-training methods, including reinforcement learning from human feedback, reinforcement learning with verifiable rewards, and on-policy distillation.
  • Build and evaluate reward models, verifiers, and large language model-as-judge pipelines.
  • Develop conversational AI environments and simulations for healthcare reinforcement learning training using synthetic data.
  • Automate post-training loops with agents for automated research.
  • Run rigorous experiments to understand what drives post-training gains.
  • Collaborate with research, engineering, and clinical teams.

About the Company

Hippocratic AI

Licenses safety-focused healthcare LLMs to institutions

Hippocratic AI builds specialized language models for healthcare that hospitals and clinics license to support non-clinical tasks and improve patient care and operations. The models are trained on evidence-based medical content and refined with a Reinforcement Learning with Human Feedback process from healthcare professionals to ensure safety and reliability. It differentiates itself by prioritizing safety, equity, and bedside-appropriate interactions, with validation on healthcare exams to ensure real-world applicability. Its goal is to improve healthcare access, equity, and outcomes by providing safe, trustworthy AI tools for clinicians and patients.

Company size
201-500
Company stage
Series C
Total funding
$387M
Headquarters
Palo Alto, CA
Founded
2022

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