Location: Remote
Type: Contract / Part-time
Commitment: 20 to 40 hours per week
Compensation: Up to 40 USD / hr
Project duration: 2 months, with potential extension
Availability: Immediate start
About the role
We create high-quality STEM training data for frontier AI models. Our data is used directly in training and evaluation pipelines at leading AI labs to improve model reasoning in technical domains.
We are looking for experts in Physics to design rigorous, deterministic problems that are genuinely challenging for state-of-the-art AI systems. Each problem must have exactly one verifiable correct answer and be submitted together with a complete, verified solution.
What you’ll do
Design advanced physics problems for frontier AI training and evaluation
Create deterministic problems with exactly one correct answer
Write complete, verified solutions and clearly document the reasoning process
Develop problems that test deep physical reasoning and multi-step analysis, not just memorization
Where relevant, use Python or specialized tools to build simulations, models, or computational workflows
Ensure all outputs are technically precise, reproducible, and well-written in English
What we’re looking for
Bachelor's, Master’s, or PhD in Physics or a closely related field
Strong research or industry experience involving theoretical, experimental, or computational physics
Strong Python skills; comfort with scientific libraries such as numpy, scipy, or similar
Solid understanding of modeling, simulation, numerical methods, and multi-step problem solving
Ability to design original, difficult problems that reflect real physics workflows
Excellent attention to detail and technical writing skills in English
Nice to have
Experience with simulation tools or domain-specific physics software (e.g., finite element tools, circuit simulators, symbolic systems)
Background in areas such as computational physics, statistical mechanics, electromagnetism, quantum mechanics, or related fields
Experience evaluating model reasoning, benchmarking, or designing technical assessments