You have opted out of job matches in .
To undo this, go to the 'Skills and Interests' section of your preferences.
Shared by 
about 1 month ago
Requirements and responsibilities
About the roleWe 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 doDesign advanced physics problems for frontier AI training and evaluationCreate deterministic problems with exactly one correct answerWrite complete, verified solutions and clearly document the reasoning processDevelop problems that test deep physical reasoning and multi-step analysis, not just memorizationWhere relevant, use Python or specialized tools to build simulations, models, or computational workflowsEnsure all outputs are technically precise, reproducible, and well-written in EnglishWhat we’re looking forBachelor's, Master’s, or PhD in Physics or a closely related fieldStrong research or industry experience involving theoretical, experimental, or computational physicsStrong Python skills; comfort with scientific libraries such as numpy, scipy, or similarSolid understanding of modeling, simulation, numerical methods, and multi-step problem solvingAbility to design original, difficult problems that reflect real physics workflowsExcellent attention to detail and technical writing skills in EnglishNice to haveExperience 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 fieldsExperience evaluating model reasoning, benchmarking, or designing technical assessments
Optionally, you can add more information later (benefits, pre-screening questions, etc.)