Remote Pod Lead - Physics

Turing; Turkey; On-site; TRY 4,129,000 - 7,569,000; Full time

Job summary

Turing is seeking an experienced Physics expert to lead a pod of trainers building realistic, terminal-based scientific tasks. You will review every task for physical correctness, reproducibility, and grader reliability, and coach trainers to deliver high-quality, challenging tasks.

You will onboard and mentor trainers, allocate work, track quality, and maintain pod documentation. Strong background in physics with programming and mentoring experience is required for this contractor role.

Qualifications

• PhD or equivalent in physics.
• Strong programming skills in Python, C/C++, Julia or Fortran with Linux.
• 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.

Responsibilities

• Review trainer tasks end to end including statements, datasets, simulation inputs, environments, reference solutions, and automated tests.
• Verify tasks reflect authentic physics workflows and require multi-step reasoning.
• Check tools, libraries, and dependencies are pinned and results are reproducible.
• Validate dimensional consistency, unit systems and conventions, conservation laws, symmetries, energy drift, convergence, and uncertainties.
• Ensure tolerances reflect legitimate stochastic and numerical variation without physically incorrect results.
• Ensure graders verify observables, fitted parameters, uncertainties, or invariants rather than formatting.
• Prevent tasks from being gamed by 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 upscale blockers.
• Maintain pod documentation covering guidelines, recurring issues, and exemplary tasks.

Skills

Python

C/C++

Julia

Fortran

Linux

Computational physics

Code review

Education

PhD in Physics

Tools

QuTiP

Qiskit

Kwant

ITensor

ROOT

Geant4

Astropy

Dedalus

MEEP

Docker

Git

CI systems

Job description

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 upscale 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.
• 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]