Requirements and responsibilities
Help shape the future of AI by designing challenging math problems that test and improve reasoning in cutting-edge models.Location: RemoteType: Contract / Part-timeCommitment: 20 hours per weekCompensation: Up to 40 USD / hrProject duration: 2 months, with potential extensionAvailability: Immediate startAbout 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 Mathematics 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 mathematical 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 mathematical reasoning, not just memorizationWhere relevant, use Python or specialized tools to build computational workflowsEnsure all outputs are technically precise, reproducible, and well-written in EnglishWhat we’re looking forMaster’s or PhD in Mathematics or a closely related fieldStrong research or industry experience involving mathematical modeling, proof-based reasoning, applied mathematics, or computational mathematicsStrong Python skills; comfort with libraries such as numpy, scipy, pandas, or similarSolid grasp of algorithms, numerical methods, and computational approachesAbility to design original, difficult problems that mirror real mathematical workflowsExcellent attention to detail and technical writing skills in EnglishNice to haveExperience with symbolic math systems, theorem provers, optimization solvers, or other mathematical softwareBackground in olympiad-level, graduate-level, or research-level problem designExperience evaluating model reasoning, benchmarking, or technical assessment design
Optionally, you can add more information later (benefits, pre-screening questions, etc.)