Teams at Hippocratic AI

Build reliable training, evaluation, deployment, and data-feedback pipelines for a recursively self-improving machine learning system. Design reward and feedback signals, mitigate reward hacking and distribution drift, develop evaluation harnesses, debug model regressions, stabilize non-stationary learning loops, and ship production ML systems. The role requires strong Python and machine learning engineering fundamentals, reinforcement learning knowledge, production experience, and familiarity with feedback-driven systems such as RLHF, RLAIF, active learning, or agent evaluation.

Lead end-to-end post-training for LLMs using reinforcement learning and on-policy distillation to improve clinical reasoning, safety, and alignment. Design RL/OPD methods, build reward models and verifiers, create healthcare conversational environments with synthetic data, automate post-training loops, run experiments, and collaborate with research, engineering, and clinical teams to deploy models at scale.

Lead AI deployments for payor customers from planning through launch and adoption. Coordinate cross-functional teams, manage timelines, risks, training, stakeholder alignment, and implementation readiness. Translate payor workflows and business priorities into actionable plans, resolve blockers, guide organizational change, and develop repeatable deployment playbooks. The role requires regular customer travel and close collaboration with product, engineering, implementation, customer success, and clinical teams.