Physics Expert - AI Trainer (Mexico)

• 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

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

• 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