Physics Pod Lead

Lead a pod of 5 to 10 trainers developing terminal-based physics tasks for AI training and evaluation. Ensure physical correctness, reproducibility and reliable automated grading.

• Review task statements, datasets, simulation inputs, environments, reference solutions and automated tests.
• Validate units, dimensional consistency, conservation laws, symmetries, convergence and uncertainty analysis.
• Ensure numerical tolerances and graders assess meaningful physical results and cannot be bypassed.
• Mentor trainers, provide actionable feedback and track revisions.
• Allocate work, monitor quality and throughput, and maintain documentation.

• Ph.D., postdoctoral experience or equivalent advanced experience in physics.
• Strong Python, C/C++, Julia or Fortran skills, with Linux proficiency.
• Hands-on computational physics, simulation or physics data analysis experience.
• Experience reviewing scientific code, analyses or research outputs.
• Experience leading small technical teams and excellent written communication.

QuTiP, Qiskit, Kwant, ITensor, ROOT, Geant4, Astropy, Dedalus or MEEP;
Docker, Git, automated testing, benchmarks or AI evaluation experience.

Interest check form, AI interview and delivery review.