Mathematics AI Training Expert (Ecuador)

Remote, USA • Full-time • Posted 2026-05-31
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Help shape the future of AI by designing challenging math problems that test and improve reasoning in cutting-edge models.
Location: Remote

Type: Contract / Part-time

Commitment: 20 hours per week

Compensation: Up to 40 USD / hr

Project duration: 2 months, with potential extension

Availability: Immediate start

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 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 do
Design advanced mathematical 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 mathematical reasoning, not just memorization

Where relevant, use Python or specialized tools to build computational workflows

Ensure all outputs are technically precise, reproducible, and well-written in English

What we’re looking for
Master’s or PhD in Mathematics or a closely related field

Strong research or industry experience involving mathematical modeling, proof-based reasoning, applied mathematics, or computational mathematics

Strong Python skills; comfort with libraries such as numpy, scipy, pandas, or similar

Solid grasp of algorithms, numerical methods, and computational approaches

Ability to design original, difficult problems that mirror real mathematical workflows

Excellent attention to detail and technical writing skills in English

Nice to have
Experience with symbolic math systems, theorem provers, optimization solvers, or other mathematical software

Background in olympiad-level, graduate-level, or research-level problem design

Experience evaluating model reasoning, benchmarking, or technical assessment design

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