Client Roles
ABOUT THE ROLE
Join a healthtech team developing clinical decision-support tools for use in healthcare settings. In this hands-on research role, you will build reinforcement learning environments and evaluations that help model-training teams improve clinical reasoning, with a focus on safe, measurable performance.
WHAT YOU'LL DO
- Design and build reinforcement learning environments for complex clinical reasoning, including multi-step tasks, tool use, state transitions, and measurable outcomes.
- Create 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 implementation, evaluation, and delivery to model-training teams.
- Run post-training and agent-evaluation experiments, then use observed failures to improve the environments.
- Collaborate with clinicians and technical leadership to validate model outputs and guide the work.
WHAT WE'RE LOOKING FOR
- A PhD in computer science, machine learning, or a related field, or at least three years of full-time experience in reinforcement learning research or post-training.
- At least three years of research or industry experience, or current final-year PhD or postdoctoral status, and a bachelor's degree or higher in a relevant technical field.
- Hands-on experience building RL environments for post-training or agent evaluation, including ownership of reward functions, grading rubrics, or LLM-judge calibration.
- Production-level Python, PyTorch, and Transformers ecosystem skills, w