Senior Applied Scientist at Hippocratic AI in Seattle, WA

Job description

About the role 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. What you'll do • 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. What we're looking for Must-Haves: 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). Systems and evaluation instinct. • 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. Strongest signal Bonus, not required: 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 Nice to Have: • 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. Why Join Hippocratic AI 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