Remote Pod Lead - Physics

Turing

1d•Remote

About The Position

Turing is seeking 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 produced by the trainers for physical correctness, reproducibility, and grader reliability, and coaches trainers to deliver high-quality, challenging tasks consistently. The role includes end-to-end review of task statements, datasets, simulation inputs, environments, reference solutions, and automated tests. The lead verifies that tasks reflect authentic physics workflows and require genuine multi-step reasoning, and that tools, libraries, and dependencies are pinned so results are reproducible. The role also validates dimensional consistency, unit systems and conventions, conservation laws, symmetries, integrator energy drift, convergence, and statistical treatment of uncertainties. Graders should assess scientific outputs rather than formatting, and tasks should not be solvable through hard-coded outputs or bypassed computation. The Pod Lead provides actionable written feedback, tracks revisions through closure, onboards and mentors trainers, allocates work, monitors quality and throughput, escalates blockers, and maintains pod documentation covering guidelines, recurring issues, and exemplary tasks. This is a contractor assignment for a four-week contract. The commitment is at least four hours per day and a minimum of 40 hours per week, with four hours of overlap with PST. The expected start date is next week. The assignment does not include medical or paid leave.

Requirements

Ph.D., postdoctoral experience, or equivalent advanced experience in physics.
Strong programming skills in Python, C/C++, Julia, or Fortran, and 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.

Nice To Haves

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.
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.

Responsibilities

Review trainer tasks end to end, including task statements, datasets, simulation inputs, environments, reference solutions, and automated tests.
Verify that tasks represent 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 accommodate 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.