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Hippocratic AI is building safety-focused generative AI for healthcare, deployed at scale across health systems, payors, and pharma. The company is making a significant, multi-year investment in reinforcement learning (RL) infrastructure and is now scaling the operation behind it.
You will serve as Head of RL Operations, a newly created senior leadership role responsible for building and running the RL operation end to end. This is a rare opportunity to build a major, board-backed operation from the ground up with existing resources, mandate, and an expert network already in place.
Key Responsibilities:
• Own the full RL operation end to end: strategy, design, execution, quality, throughput, and cost. Translate research and engineering team needs into well-run programs that deliver high-quality data and signal on schedule.
• Build and lead the team, starting with roughly ten people with four to five direct reports from day one, growing substantially as the program scales. Hire senior leaders, set structure and operating model, and create a culture of rigor and ownership.
• Manage an existing gig-economy network of expert labelers. Grow the network, manage quality, and deepen domain coverage across clinical and non-clinical specialties.
• Drive the build of software interfaces and platforms that labelers and reviewers work in, partnering closely with Engineering. Define product requirements based on complex annotation and RL workflows.
• Take an active role in training models directly, working with Research to turn operational data into model improvements and close the loop between labeling, evaluation, and training.
• Plan and execute growth to very large scale, including capacity planning, budget ownership, in-house talent sourcing and onboarding, and systems to maintain quality as volume grows.
• Establish operating cadence, metrics, and reporting that leadership and the board rely on.
Why This Matters:
Hippocratic AI is building AI that touches patient lives. The bar for safety and quality is not a checkbox—it is the foundation of everything shipped. You will shape how safe, reliable healthcare AI is trained at scale.
Requirements:
• Bachelor's degree in Computer Science, Machine Learning, or related field.
• 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 than years of experience or company size.
• Experience as a VP, SVP, or equivalent senior leader at a data-labeling company, hyperscaler, or frontier model lab. If 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 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. 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 Have:
• 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.
