Machine Learning Engineer

Hippocratic AI • Menlo Park, CA

3d

About The Position

We are building a recursive self-improvement system — a machine learning system that iteratively improves itself through feedback, evaluation, and automated learning loops. You will help build the engineering pipeline that keeps these loops fast, reliable, and trustworthy: the training and evaluation pipelines, the reward and feedback signals, and the safeguards that prevent a self-improving system from silently degrading or gaming its objectives.
This is an engineering-first role with deep reinforcement learning requirements. You should be equally comfortable writing robust production ML code and reasoning about reward design, credit assignment, and why feedback-driven systems become unstable.

Requirements

• Strong MLE fundamentals (non-negotiable)
• Excellent Python and clean, well-tested ML training code.
• Solid grasp of data pipelines, distributed / large-scale training, and experiment tracking.
• The instinct and skill to debug why a model silently got worse — not just why it crashed.
• Hands-on experience with a feedback or learning loop (at least one).
• Built or owned part of a feedback loop — a reward model, an evaluation harness, or the data pipeline for an RLHF/RLAIF or active-learning system.
• Ran a retraining or continual-learning pipeline where a model consumed its own predictions or production data (e.g. ranking, recommendations, fraud, spam).
• Fine-tuned LLMs with human or AI feedback, or built agentic evaluation harnesses.
• Reinforcement learning foundations and curiosity.
• Working knowledge of reward modeling, on-policy vs. off-policy tradeoffs, and credit assignment (does not need to be a research-level RL expert).
• Has seen — or can reason clearly about — feedback-system failure modes: reward hacking, specification gaming, feedback loops amplifying errors.
• Comfortable evaluating non-stationary systems (systems whose behavior and data distribution change over time).
• Builds the eval before the model; treats measurement as a first-class deliverable.
• Has shipped an ML system into production and kept it healthy over time.

Nice To Haves

• The ideal candidate has built or shipped a full system that improved from its own outputs or feedback end to end. This is rare at this level, so treat it as a standout differentiator rather than a filter. Examples: RLHF / RLAIF pipelines, Self-play systems, Active-learning loops, Automated data flywheels, Agentic evaluation harnesses.
• PhD or MS in RL / ML paired with real production experience (either the science or the engineering half alone is fine if the other is strong).
• Experience at a lab or company doing RLHF, agents, or large-scale ML infrastructure.
• Familiarity with LLM fine-tuning, evaluation frameworks, or agent orchestration.

Responsibilities

• Build and maintain the training, evaluation, and deployment loops at the core of the self-improvement system, with a strong emphasis on reproducibility and reliability.
• Design and implement reward and feedback signals; investigate and mitigate reward hacking, specification gaming, and distribution drift.
• Build evaluation harnesses and metrics before models — because a self-improving system is only as safe as its measurement of “better.”
• Own data pipelines and automated data flywheels that feed the learning loop.
• Debug subtle model-quality regressions and stabilize training and feedback loops that go non-stationary.
• Collaborate with research and product to turn methods into robust, shippable systems.

Benefits

• Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.
• Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.
• Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others.
• Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies — ensuring our platform is powerful, trusted, and truly transformative.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior