reinforcement learning · healthtech
ABOUT THE ROLE
Join a healthtech team developing clinical decision-support tools for use in healthcare settings. You will build reinforcement learning environments and evaluations that help model-training teams improve clinical reasoning, with work validated alongside clinical experts.
WHAT YOU'LL DO
- Build reinforcement learning environments for complex clinical reasoning, including multi-step tasks, tool use, state transitions, and measurable outcomes.
- Design and calibrate reward functions, grading rubrics, and LLM judges, and test them for reward hacking and misleading scores.
- Own the workflow from task design and data creation through environment implementation, evaluation, and delivery to model-training teams.
- Run post-training and agent evaluation experiments, then use observed failures to improve environments.
- Collaborate with clinicians and technical leadership to validate model outputs and improve the work.
WHAT WE'RE LOOKING FOR
- A PhD in computer science, machine learning, or a related field, or at least 3 years of full-time reinforcement learning research or post-training experience; candidates should also have 3 or more years of research or industry experience, or be a final-year PhD student or postdoctoral researcher.
- Hands-on experience building reinforcement learning environments for post-training or agent evaluation, and owning reward functions, grading rubrics, or LLM-judge calibration.
- Production-level Python, PyTorch, and Transformers ecosystem skills, plus experience with LLMs, reinforcement learning, post-training, ev