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Hippocratic AI is building a recursive self-improvement system—a machine learning platform that iteratively enhances itself through feedback, evaluation, and automated learning loops. As a Machine Learning Engineer, you will own the engineering pipeline that keeps these loops fast, reliable, and trustworthy: training and evaluation pipelines, reward and feedback signals, and safeguards that prevent silent degradation or objective gaming.
This is an engineering-first role requiring deep reinforcement learning expertise. You'll be equally comfortable writing robust production ML code and reasoning about reward design, credit assignment, and feedback-system stability.
Key responsibilities include:
- Building and maintaining training, evaluation, and deployment loops with emphasis on reproducibility and reliability
- Designing reward and feedback signals; investigating and mitigating reward hacking, specification gaming, and distribution drift
- Building evaluation harnesses and metrics before models—treating measurement as a first-class deliverable
- Owning data pipelines and automated data flywheels that feed the learning loop
- Debugging model-quality regressions and stabilizing non-stationary training and feedback loops
- Collaborating with research and product to ship robust systems
Required qualifications:
- Strong MLE fundamentals: excellent Python, clean well-tested ML training code, solid grasp of data pipelines, distributed training, and experiment tracking
- Hands-on experience building or owning feedback loops (reward models, evaluation harnesses, RLHF/RLAIF pipelines, active-learning systems, or retraining pipelines)
- Reinforcement learning foundations: working knowledge of reward modeling, on-policy vs. off-policy tradeoffs, credit assignment, and feedback-system failure modes
- Systems and evaluation instinct: ships ML systems into production and maintains them over time
Bonus qualifications include PhD/MS in RL/ML with production experience, background at labs doing RLHF or large-scale ML infrastructure, and familiarity with LLM fine-tuning or agent orchestration.
Hippocratic AI is building the world's first healthcare-only, safety-focused LLM to transform patient outcomes globally. The company was co-founded by CEO Munjal Shah and a team of physicians, hospital leaders, and AI pioneers from Stanford, Google, Meta, Microsoft, and NVIDIA. Recently raised $126M Series C at $3.5B valuation with $404M total funding from CapitalG, General Catalyst, a16z, Kleiner Perkins, and leading healthcare investors.
