TuringDomain experts
Pod Lead: Physics(Terminal Bench Science)
Pay not published
About this role, in Turing's words
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.
Show all 11 itemsHide the other 8 in What you'll do
- 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.
Candidate background
What we're looking for
- 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
Show all 3 itemsHide these 3 items in 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 PointsPreferred
Show all 3 itemsHide these 3 items in 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.
Quoted from selected sections of Turing's posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in Turing's words
Posted by Turing, reproduced here so you can judge the role before clicking. Original posting ↗
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.
What we're looking for:
• 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.
Offer Details:
• 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]
Working with TuringPay, getting started and work, from Turing's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Paid monthly, around the 5th, straight to your bank account, or inside Deel on projects run through Deel. Its help articles disagree on whether that is the 5th calendar day or business day. help.turing.com/articles/9691407757-project-onboarding, help.turing.com/articles/6582454854-what-if-i-haven-t-rec..., help.turing.com/articles/1226770947-project-offboarding
- Before your first paid task
- A short application, with tests on some roles. Turing's review article says an interview is typically required for all roles. Onboarding covers the terms, ID verification with a driver's license or passport, and tax and payment details. help.turing.com/articles/2020987592-apply-for-jobs-at-turing, help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/9691407757-project-onboarding
- How much work
- Review takes from 48 hours to 6 weeks after you finish applying, and the client makes the final choice. No reply within 6 weeks usually means you were not selected. help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/2020987592-apply-for-jobs-at-turing
- Contract
- Depending on the project, the engagement rests on Turing's Terms of Service and a Service Agreement, on Deel, or on a partner that provides the contract. help.turing.com/articles/6582454854-what-if-i-haven-t-rec...
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
How does the pay compare?Across Domain experts roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Turing's posting. How we count.
Pay across the field: Domain experts
Across 752 roles in this field with a stated hourly rate in US dollars, on 21 platforms, the middle half pays $63 to $125 an hour at the top of the range, and the median is $100.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| micro1 | 155 | $12 to $400 · median $110 |
| Alignerr | 148 | $24 to $200 · median $80 |
| AfterQuery | 93 | $45 to $175 · median $100 |
| Mercor | 90 | $55 to $450 · median $120 |
| Handshake AI | 81 | $30 to $300 · median $80 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 23 Oct 2026, and still on Turing's list when we last read it on 25 Oct 2026.
- Posted by Turing
- First seen by AIPayList
- Last seen live
- Listing ID
- 115911
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