Member of Technical Staff, Reinforcement Learning at True Talent Solutions Partners Remote

True Talent Solutions Partners; United States; Remote; USD 250,000 - 400,000; Full time

Job summary

True Talent Solutions Partners seeks a PhD or experienced RL researcher to own RL environments for clinical decision‑support products. You will design reward structures, grading rubrics, and language model judges, and ensure robust evaluation across post‑training settings.

Responsibilities include end‑to‑end development from data creation to deployment, plus collaboration with clinical experts. Remote work is available for Canada/US applicants.

Qualifications

• PhD in computer science, machine learning, or related field, or at least 3 years of full‑time reinforcement learning research or post‑training experience.
• Hands‑on experience building reinforcement learning environments for post‑training or agent evaluation.
• Production‑level Python, PyTorch, and Transformers ecosystem experience.

Responsibilities

• Own reinforcement learning environments and evaluations for multi‑step clinical reasoning.
• Design and calibrate reward functions and language model judges, test for reward hacking.
• Lead the full loop from task design and data creation to environment implementation and delivery to model training teams.
• Run post‑training and agent evaluation experiments and improve environments from observed failures.
• Collaborate with clinical experts to validate model outputs and improve decision‑support systems.

Skills

Python

PyTorch

Transformers

Education

PhD in CS/ML or related field

Bachelor’s in CS/Math or related field

Tools

Transformers ecosystem

Job description

About the Role

Join a healthtech team building decision‑support tools for clinical care. You will own reinforcement learning environments and evaluations that help training teams develop systems for complex clinical reasoning, with work informed by clinical experts.

What You'll Do

Build reinforcement learning environments for multi-step clinical reasoning, including tool use, state transitions, and measurable success criteria. Design and calibrate reward functions, grading rubrics, and language model judges, and test them for reward hacking and misleading scores. Own the full loop 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 clinical experts to validate model outputs and improve the quality of decision‑support systems.

What We're Looking For

A PhD in computer science, machine learning, or a related field, or at least three years of full‑time reinforcement learning research or post‑training experience. At least three years of research or industry experience, or current status as a final‑year PhD candidate or postdoctoral researcher. Hands‑on experience building reinforcement learning environments for post‑training or agent evaluation, ideally in a frontier AI lab or leading AI company. Production‑level Python, PyTorch, and Transformers ecosystem experience. Experience owning reward functions, grading rubrics, or language model judge calibration. A bachelor’s degree or higher in computer science, mathematics, or a related technical field. Ability to communicate failed experiments and uncertainty clearly, validate plausible model outputs, and work across disciplines in ambiguous settings. Clinical AI experience is useful, as is knowledge of information retrieval design, constraint solving, SAT/SMT, or domain‑specific languages. Authorization to work remotely in Canada or the United States, and willingness to attend in‑person offsites several times a year.

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.