Review AI-generated mathematical solutions, proofs, and quantitative reasoning for correctness, clarity, and adherence to prompts
Evaluate step-by-step mathematical reasoning and identify errors in calculations, proofs, assumptions, methodology, or conceptual understanding
Generate high-quality mathematical explanations, model solutions, and structured reasoning workflows demonstrating correct approaches
Analyze AI responses involving pure mathematics, applied mathematics, statistics, probability, optimization, modeling, and discrete mathematics
Fact-check mathematical claims, formulas, derivations, and quantitative information across multiple domains
Compare and rank multiple AI-generated responses based on mathematical correctness, rigor, reasoning quality, and explanatory clarity
Produce expert-level mathematical content that communicates advanced concepts clearly and accurately
Support AI model improvement through mathematical annotation workflows, evaluation tasks, quality assurance reviews, and structured documentation
Contribute to the development of prompts and benchmark solutions designed to improve mathematical reasoning in AI systems
Requirements
Education: MS or PhD in Mathematics, Statistics, or a related quantitative field from one of the top 100 universities
Strong expertise in pure mathematics, applied mathematics, proofs, mathematical modeling, statistics/probability, and optimization
Excellent problem-solving and analytical reasoning skills with the ability to evaluate complex mathematical logic systematically
Ability to clearly communicate advanced mathematical concepts and translate theory into understandable real-world explanations
Strong fact-checking and verification skills for quantitative and mathematical information
Background in research, analytical writing, programming, debate, or advanced mathematical problem-solving preferred
Excellent English writing skills with Minimum C1 English proficiency required
For PhD holders: experience developing or critically reviewing advanced mathematical materials such as proofs, textbook sections, research notes, or problem banks
Previous experience with AI data training, RLHF, model evaluation, or annotation workflows is a strong plus
Highly detail-oriented with the ability to identify subtle logical, computational, or methodological issues consistently