Head of Reinforcement Learning Operations

Hippocratic AI is building safety-focused generative AI for healthcare, deployed at scale across health systems, payors, and pharma. We are a technology and product company, and the quality of our models depends on the quality of the human expertise and data that train and evaluate them. Hippocratic AI is making a significant, multi-year investment in RL infrastructure, one of the largest commitments in the company’s history.

We have made real progress on the technology and are now scaling the operation behind it. We are hiring a Head of RL, a newly created senior leadership role, to build and run that operation end to end. We are building this capability in-house. Data labeling has changed dramatically as LLMs have advanced. Today it is complex, expert-driven work that spans software interfaces, platforms, and in some cases training models directly.

This role is for a leader who has already built an operation like this at scale, was there through the build-out phase, and can do it again here.

Own the full RL operation: strategy, design, execution, quality, throughput, and cost. Translate the needs of our research and engineering teams into well-run programs that deliver high-quality data and signal on schedule, and build the operating cadence, metrics, and reporting that leadership and the board rely on.

Build and lead the team, starting at roughly ten people with four to five direct reports from day one and growing substantially as the program scales. Hire senior leaders, set the structure and operating model, and create a culture of rigour and ownership.

We have already built a gig‑economy network of expert labelers. You will inherit this network as a core resource and be responsible for growing it, managing quality, and deepening domain coverage across clinical and non‑clinical specialties.

Drive the build of the software interfaces and platforms that labelers and reviewers work in, partnering closely with Engineering. Define product requirements based on how complex annotation and RL workflows actually run, and push for tooling that improves quality and efficiency.

Take an active role in training some of our models directly, working hand in hand with Research to turn operational data into model improvements and to close the loop between labeling, evaluation, and training.

Plan and execute the growth of the program to a very large scale, including capacity planning, budget ownership, in‑house sourcing and onboarding of talent, and the systems needed to maintain quality as volume grows.

• 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. Scale of operations, scope of impact, and depth of knowledge matter more to us than years of experience or company size.
• Experience as a VP, SVP, or equivalent senior leader at a data-labeling company, a hyperscaler, or a frontier model lab. If you come from a labeling company, operations with tens of millions of dollars in revenue or comparable scale.
• Presence through a build‑out phase: you have taken a function from early stage to a large, mature operation, not only run one that already existed.
• 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…