Machine Learning Engineer (Reinforcement Learning)

Location

Location: Menlo Park, CA, On-site

Company

Hippocratic AI is a healthcare-focused AI startup building a safety-first large language model platform to transform patient outcomes globally.

What you will do

• Build and maintain training, evaluation, and deployment loops emphasizing reproducibility and reliability.
• Design and implement reward and feedback signals; mitigate reward hacking and specification gaming.
• Develop evaluation harnesses and metrics to measure model improvements.
• Own data pipelines and automated data flywheels feeding the learning loop.
• Debug model-quality regressions and stabilize non-stationary training and feedback loops.
• Collaborate with research and product teams to deliver robust, shippable ML systems.

Requirements

• On-site work in Menlo Park, CA
• Strong machine learning engineering fundamentals with excellent Python and clean, well-tested ML training code.
• Experience with data pipelines, distributed/large-scale training, and experiment tracking.
• Hands-on experience with feedback or learning loops such as reward models, evaluation harnesses, or RLHF/RLAIF pipelines.
• Working knowledge of reinforcement learning foundations including reward modeling and credit assignment.
• Experience shipping ML systems into production and maintaining system health over time.

Nice to have

• PhD or MS in RL/ML with production experience.
• Experience at labs or companies working on RLHF, agents, or large-scale ML infrastructure.
• Familiarity with LLM fine-tuning, evaluation frameworks, or agent orchestration.

Culture & Benefits

• Work with a team of AI pioneers, physicians, and healthcare leaders.
• Backed by leading healthcare and AI investors with significant funding.
• Equal opportunity employer committed to diversity and inclusion.
• Support for accommodations during the hiring process.