Head of Reinforcement Learning Operations

Hippocratic AI•Menlo Park, CA

1d•Onsite

About The Position

Hippocratic AI is seeking a Head of Reinforcement Learning (RL) Operations, a newly created senior leadership role, to build and run their RL operations end to end. The company is making a significant, multi-year investment in RL infrastructure and needs a leader experienced in scaling complex, expert-driven data operations involving software interfaces, platforms, and direct model training. This role is crucial for ensuring the quality of AI models used in healthcare by managing the human expertise and data that train and evaluate them.

Requirements

Bachelor's degree in Computer Science, Machine Learning, or a related field.
A track record of building and scaling a large RL, data, or human-feedback operation.
Experience as a VP, SVP, or equivalent senior leader at a data-labeling company, a hyperscaler, or a frontier model lab (e.g., operations with tens of millions of dollars in revenue or comparable scale if from a labeling company).
Experience leading a function from an early stage to a large, mature operation.
Deep understanding of modern LLM labeling and RL workflows, including expert annotation, quality control, evaluation, and how human feedback translates into model gains.
Ability to manage complex, multi-faceted operations end to end, spanning people, process, tooling, and models.
Strong technical fluency and credibility with Research and Engineering teams, with experience shaping internal software platforms.
Proven ability to recruit, develop, and lead senior managers and large distributed workforces, including contractor and gig-based expert networks.
Strong judgment, financial discipline, and executive communication skills.
Comfortable owning a large budget and reporting to senior leadership and the board.
Willingness to work on-site five days a week in Menlo Park, CA; local candidates preferred.

Nice To Haves

Experience training or fine-tuning models directly, including RLHF, RLAIF, or reward modeling.
Experience managing clinical or other highly credentialed expert labeler populations.
Background in healthcare, regulated industries, or safety-critical AI.

Responsibilities

Own the full RL operation: strategy, design, execution, quality, throughput, and cost.
Translate research and engineering needs into well-run programs delivering high-quality data and signal on schedule.
Establish operating cadence, metrics, and reporting for leadership and the board.
Build and lead the RL operations team, starting with approximately ten people and growing substantially.
Hire senior leaders, set the team's structure and operating model, and foster a culture of rigor and ownership.
Grow, manage quality, and deepen domain coverage of the existing expert labeler network.
Drive the development of software interfaces and platforms for labelers and reviewers in partnership with Engineering.
Define product requirements for annotation and RL workflows to improve quality and efficiency.
Take an active role in training models directly, collaborating with Research to translate operational data into model improvements.
Plan and execute the growth of the program to a very large scale, including capacity planning, budget ownership, talent sourcing, and quality maintenance systems.