Lead a pod of trainers writing terminal-based physics tasks and check each task for physical correctness, reproducibility and grader reliability. It suits physicists with a PhD or equivalent who have reviewed code or research and mentored technical teams.
About the role
About Turing:
Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.
Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Role Overview:
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.
What you'll do:
- 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.
- Maintain pod documentation covering guidelines, recurring issues, and exemplary tasks.
- 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
- Excellent written communication skills for providing precise, constructive feedback
- 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 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.
- Prior experience in AI training or evaluation programs.
- 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 week [expected start date is next week]
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