Physics Pod Lead

Crossing Hurdles; India; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Crossing Hurdles seeks a Physics Pod Lead to guide a pod of 5–10 trainers building terminal-based physics tasks for AI training and evaluation, ensuring physical correctness and reliable automated grading. You will review task statements, datasets, simulations and tests, validate units, conservation laws and uncertainties, and mentor the team to maintain high quality and throughput.

Required: PhD or equivalent, strong Python, C/C++, Julia or Fortran, Linux proficiency, and hands-on computational

Qualifications

• PhD, postdoctoral experience or equivalent advanced physics experience.
• Strong programming skills in Python, C/C++, Julia or Fortran.
• Linux proficiency and hands-on computational physics experience.
• Experience reviewing scientific code, analyses or research outputs.

Responsibilities

• Lead a pod of 5–10 trainers developing terminal-based physics tasks for AI training and evaluation.
• Review task statements, datasets, simulation inputs, environments, reference solutions and automated tests.
• Validate units, dimensional consistency, conservation laws, symmetries, convergence and uncertainty analysis.
• Mentor trainers, provide actionable feedback and track revisions.
• Allocate work, monitor quality and throughput, and maintain documentation.

Skills

Python

C/C++

Julia

Fortran

Linux

Computational physics

Code review

Education

PhD or equivalent advanced physics experience

Tools

QuTiP

Qiskit

Kwant

ITensor

ROOT

Geant4

Astropy

Dedalus

MEEP

Docker

Git

Job description

Physics Pod Lead

About the role

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.

Responsibilities

• 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.

Requirements

• 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.

Preferred

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

Selection

Interest check form, AI interview and delivery review.