Remote Pod Lead - Physics

About the role

Turing, an AI company, is hiring an experienced Physics expert to lead a pod of trainers building realistic, terminal-based scientific tasks for Terminal Bench Science. The pod lead reviews every task for physical correctness, reproducibility and grader reliability, coaches trainers to deliver high-quality, challenging tasks, and manages the pod's workflow.

Key responsibilities

• Review trainer tasks end to end, including task statements, datasets, simulation inputs, environments, reference solutions and automated tests.
• Verify that tasks reflect authentic physics workflows and require genuine multi-step reasoning.
• Check that tools, libraries and dependencies are pinned and that results are reproducible.
• Validate dimensional consistency, unit systems, conservation laws, symmetries, integrator energy drift, convergence and statistical treatment of uncertainties.
• Ensure tolerances reflect legitimate stochastic and numerical variation, and that graders check observables, fitted parameters and uncertainties rather than formatting.
• Ensure tasks cannot be gamed through hard-coded outputs or bypassed computation.
• Provide clear, actionable written feedback and track revisions through closure.
• Onboard and mentor trainers, allocate work, track quality and throughput and escalate blockers.
• Maintain pod documentation covering guidelines, recurring issues and exemplary tasks.

Required profile

• Ph.D., postdoctoral experience or equivalent advanced experience in physics.
• Hands-on experience with computational physics, simulation or physics data analysis.
• Proven experience reviewing code, analyses or research outputs.
• Experience mentoring or leading small technical or research teams.
• Excellent written communication skills for precise, constructive feedback.

Required skills

• Strong programming in Python, C/C++, Julia or Fortran, with Linux proficiency.
• Preferred: familiarity with QuTiP, Qiskit, Kwant, ITensor, ROOT, Geant4, Astropy, Dedalus, MEEP or similar tools.
• Preferred: familiarity with Docker, Git, CI systems and automated testing.
• Bonus: experience designing benchmarks or simulation-based evaluations, evaluating AI coding or terminal agents, or prior experience in AI training or evaluation programs.

Offer details

• Contractor assignment, no medical or paid leave.
• At least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST time.
• Duration of 4 weeks, expected start next week.