Member of Technical Staff, Reinforcement Learning

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, evaluation, or reward modeling.

• A bachelor's degree or higher in computer science, machine learning, mathematics, or a related technical field.

• Clear technical writing, including the ability to explain failed experiments and uncertainty, and a careful approach to validating plausible model outputs.

• Comfort working across disciplines in an ambiguous environment and attending in-person offsites several times per year.

• Clinical AI experience is useful. Knowledge of information retrieval, constraint solving, SAT/SMT, or domain-specific languages is also relevant.

• Must be authorized to work in the United States or Canada. Visa sponsorship is not available.

Compensation & Benefits

Base salary: $250,000 to $400,000 USD annually. Visa sponsorship is not available.

Location

Remote, with the primary location in Canada. Candidates must be authorized to work in Canada or the United States.