Train AI on Mathematical Reasoning.
The models shaping the future of AI are only as rigorous as the mathematicians behind them. Apply your expertise where it matters- and get paid to do it.
Stephen
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Work that impacts your future.
The largest language models in the world still fail at undergraduate-level mathematics. You're the feedback loop that fixes this — one proof at a time.
Three steps.
No overhead.
Apply & Qualify
Complete a math assessment. We evaluate your proof-writing, problem-solving, and ability to identify logical errors. No resume required.
Get Matched
Based on your assessment results, you're matched to projects in your areas of strength. Calculus, algebra, topology, number theory — you choose what fits.
Work & Get Paid
Complete tasks on your own schedule. Each task has clear specifications and a defined scope. Payment is per-task, processed weekly, starting at $40/hour.
What you'll actually do.
Write a prompt
Craft precise mathematical prompts designed to probe the boundaries of model reasoning — non-trivial problems that expose structural weaknesses and explore edge cases.
Review AI output
Evaluate model-generated solutions line by line. Identify logical gaps, missed edge cases, and incorrect theorem applications.
Write the correct solution
Author a complete, rigorous solution that demonstrates the correct reasoning path. This becomes training signal.
Where models need you most.
These are the areas where current AI systems consistently struggle. Your expertise directly addresses the hardest open problems in mathematical reasoning.
Proof-Based Reasoning
Evaluate and construct formal proofs. Identify logical gaps, invalid inferences, and incomplete case analysis in model- generated arguments.
Multi-Step Problem Solving
Review complex solutions requiring sequential reasoning. Check that each step follows validly from the last and that no intermediate steps are skipped.
Symbolic Logic & Formalization
Assess the model's ability to translate natural language into formal mathematical notation and reason within formal systems.
Edge Cases & Counterexamples
Identify where models fail on boundary conditions, degenerate cases, and subtle counterexamples that break general claims.
Quantitative Modeling
Evaluate applied mathematics problems involving optimization, statistical reasoning, and mathematical modeling of real-world systems.
Research-grade standards.
We require excellence, just as you would require in peer review. The researchers and academics on our platform aren’t here to tick boxes. They’re here because the quality standard matters to them.
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“This is evaluation by people who understand system behavior and edge cases. The focus is always on improving model reliability.”
Daniel
Research Engineer
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“Every decision is grounded in first-principles reasoning. The standards are comparable to what I’ve seen in academic and industrial research.”
Anita
Applied Scientist
“This is annotation done by people who understand models, not just labels. The quality bar is high and enforced.”
Elena
Senior ML Engineer
“The work demands subject-matter expertise. Decisions are justified, reviewed, and aligned to well-defined taxonomies.”
Stephen
Research Scientist
Paid to contributors
Expert annotators
Annotations completed
Built for people who think in proofs.
Graduate Students
PhD and Master's candidates in pure or applied mathematics.
Olympiad Competitors
National or international competition experience in creative problem solving.
Research Mathematicians
Active or former researchers applying deep expertise to AI reasoning.
Applied Math Professionals
Quants, engineers, and data scientists with rigorous math foundations.
Frequently Asked Questions
Have questions? Here we answer the most common questions.
You'll work on tasks that help AI perform better, like reviewing responses, checking accuracy, refining prompts, and rating outputs. No coding or technical background needed.
Projects vary but commonly include reviewing AI-generated text, ranking responses, checking facts, labeling data, and reviewing content in specific subject areas. All focused on improving real production AI systems.
This is ideal for people with strong reasoning skills, attention to detail, and subject-matter expertise who want flexible, remote work with real impact. Researchers, students, professionals, and independent contributors who enjoy analytical work and want to contribute to advancing AI systems.
This is a flexible, task-based contractor role. You work remotely on your own schedule, focused on output quality rather than hours logged. There is no long-term commitment required, and you can work as much or as little as you choose.
Get ahead in a changing workforce.
No recruiters. No interviews. Just meaningful work and real compensation.