Member of Technical Staff, Reinforcement Learning

About the Role

Join a healthtech team developing validated clinical decision tools for use in healthcare settings. You will build reinforcement learning environments and evaluations that support safer, more reliable clinical AI.

What You'll Do

Build RL 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 failures to improve environments.

Collaborate with researchers and clinicians who validate model outputs.

What We're Looking For

A PhD in computer science, machine learning, or a related field, or at least three years of full-time RL research or post-training experience.

At least three years of research or industry experience, or current status as a final-year PhD student or postdoctoral researcher.

Hands-on experience building RL environments for post-training or agent evaluation, including experience in a full-time role at a leading AI lab or company.

Strong technical fundamentals, with a BS or higher in computer science, machine learning, mathematics, or a related field.

Production-level Python, PyTorch, and Transformers ecosystem experience, plus familiarity with LLMs, reinforcement learning, post-training, evaluation, and reward modeling.

Experience owning reward functions, grading rubrics, or LLM judge calibration, and clearly communicating failed experiments and uncertainty in writing.

Ability to validate plausible model outputs, collaborate across disciplines, and work comfortably with ambiguity.

Clinical AI experience is helpful, as is knowledge of information retrieval design, constraint solving, SAT/SMT, or domain-specific languages.

Authorization to work in the United States or Canada is required. Willingness to attend in-person offsites multiple times per year is also required.

Compensation & Benefits

Annual base salary: $250,000 to $400,000 USD. Visa sponsorship is not available.

Location

Remote, based in Canada.