[Hiring] Pod Lead - Physics @Turing

Role Description

We are seeking an experienced Physics expert to lead a pod of trainers building realistic, terminal-based scientific tasks for Terminal Bench Science. You will review every task your trainers produce for physical correctness, reproducibility, and grader reliability, and coach trainers to consistently deliver high-quality, challenging tasks.

• 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 and conventions, conservation laws, symmetries, integrator energy drift, convergence, and statistical treatment of uncertainties.
• Ensure tolerances reflect legitimate stochastic and numerical variation without accepting physically incorrect results.
• Ensure graders verify observables, fitted parameters, uncertainties, or physical invariants 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.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced experience in physics.
• Strong programming skills in Python, C/C++, Julia, or Fortran, with Linux proficiency.
• 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 providing precise, constructive feedback.

Preferred Qualifications

• Familiarity with QuTiP, Qiskit, Kwant, ITensor, ROOT, Geant4, Astropy, Dedalus, MEEP, or similar tools.
• Familiarity with Docker, Git, CI systems, and automated testing.
• Experience designing benchmarks or simulation-based evaluations.

Bonus Points

• Experience evaluating AI coding or terminal agents.
• Experience working in large collaborations with formal internal review processes.
• Prior experience in AI training or evaluation programs.

Requirements

• Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST.
• Employment type: Contractor assignment (no medical/paid leave).
• Duration of contract: 4 weeks [expected start date is next week].