Remote Pod Lead - Physics

Turing Enterprises, Inc.; Fatih; Remote; TRY 4,988,000 - 6,983,000; Part time

Job summary

Turing Enterprises, Inc. 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 throughput, and maintain pod documentation to ensure consistent standards across tasks and evaluations.

Qualifications

• PhD or equivalent advanced physics experience.
• Strong programming in Python/C/C++, Julia or Fortran with Linux proficiency.
• Hands-on experience in computational physics, simulation, or physics data analysis.
• Proven track record reviewing code, analyses, or research outputs.
• Experience mentoring or leading small technical or research teams.

Responsibilities

• Review trainer tasks end to end including statements, datasets, simulations, environments, reference solutions and tests.
• Verify tasks reflect authentic physics workflows requiring multi-step reasoning.
• Check tool dependencies are pinned and results are reproducible.
• Validate dimensional consistency, units, conservation laws, symmetries and numerical stability.
• Ensure graders verify observables or invariants rather than formatting, and prevent hard-coded outputs.

Skills

Python

C/C++

Julia

Fortran

Linux

Mentoring

Education

Ph.D., postdoctoral experience, or equivalent advanced experience in physics

Tools

QuTiP

Qiskit

Kwant

ITensor

ROOT

Geant4

Astropy

Dedalus

MEEP

Job description

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 elevate 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]