Remote Pod Lead - Physics

Turing is a rapidly growing AI company focused on advancing and deploying powerful AI systems. The company collaborates with leading AI labs to push the boundaries of model capabilities in areas such as reasoning, coding, multimodality, multilinguality, and STEM knowledge. These advancements are then applied to develop real-world AI solutions that address critical business challenges.

We are currently looking for an experienced Physics expert to lead a team of trainers responsible for creating realistic, terminal-based scientific tasks for Terminal Bench Science. In this role, you will ensure that every task produced meets high standards of physical accuracy, reproducibility, and grading reliability. You will also mentor and guide trainers to consistently deliver challenging and high-quality scientific tasks.

Key Responsibilities:
- Review trainer-created tasks comprehensively, including task statements, datasets, simulation inputs, environments, reference solutions, and automated tests.
- Confirm that tasks represent authentic physics workflows and require genuine multi-step reasoning.
- Ensure all tools, libraries, and dependencies are properly versioned and that results are reproducible.
- Validate dimensional consistency, unit systems, conservation laws, symmetries, integrator energy drift, convergence, and statistical treatment of uncertainties.
- Verify that tolerances account for legitimate stochastic and numerical variations without accepting physically incorrect outcomes.
- Ensure graders assess observables, fitted parameters, uncertainties, or physical invariants rather than superficial formatting.
- Prevent tasks from being gamed through hard-coded outputs or bypassed computations.
- Provide clear, actionable written feedback and track revisions until completion.
- Onboard and mentor trainers to improve their skills and output quality.
- Allocate work, monitor quality and throughput, and escalate any blockers as needed.
- Maintain pod documentation including guidelines, recurring issues, and examples of exemplary tasks.

Required Qualifications:
- Ph.D., postdoctoral experience, or equivalent advanced experience in physics.
- Strong programming skills in Python, C/C++, Julia, or Fortran, with proficiency in Linux environments.
- Hands-on experience with computational physics, simulations, or physics data analysis.
- Proven ability to review code, analyses, or research outputs critically.
- Experience mentoring or leading small technical or research teams.
- Excellent written communication skills for delivering precise and constructive feedback.

Preferred Qualifications and Benefits:
- Familiarity with scientific tools such as QuTiP, Qiskit, Kwant, ITensor, ROOT, Geant4, Astropy, Dedalus, or MEEP.
- Experience with Docker, Git, continuous integration systems, and automated testing frameworks.
- Background in designing benchmarks or simulation-based evaluations.
- Bonus points for experience evaluating AI coding or terminal agents, working in large collaborations with formal internal review processes, or prior involvement in AI training or evaluation programs.

Offer Details:
- Commitment of at least 4 hours per day and a minimum of 40 hours per week, with at least 4 hours overlapping Pacific Standard Time (PST).
- Employment type is contractor assignment without medical or paid leave benefits.
- Initial contract duration is 4 weeks, with an expected start date in the near future.

This opportunity is ideal for a physics professional passionate about applying their expertise to cutting-edge AI training and evaluation, while leading a team to deliver scientifically rigorous and reproducible tasks.