Physics Expert - AI Trainer
- Role
- AI / ML
- Employment
- Part-time20–40h/week
Open to BR, ES only. Set where you work from to check your eligibility.
No BS summary
Remote, Brazil-based contract for a Physics expert to design deterministic, verifiable physics problems for AI training. Requires a Master's or PhD in Physics (or closely related), strong Python plus numpy/scipy experience, and technical writing in English. Part-time contract (20–40 hrs/wk) at $40/hr for ~2 months.
Core skills
Required skills
Optional skills
Optional languages
- 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
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 rather than memorization
- When relevant, use Python or specialized tools to build simulations, models, or computational workflows and ensure outputs are reproducible and well-written in English
- Develop problems that test deep physical reasoning and multi-step analysis
- Where relevant, use Python or specialized tools to build simulations, models, or computational workflows
What they require
- Master’s or PhD in Physics or a closely related field
- Strong research or industry experience in 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
- Excellent attention to detail and technical writing skills in English
- Master's, or PhD in Physics or a closely related field
- Strong research or industry experience involving theoretical, experimental, or computational physics
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.