Mathematics AI Evaluation Specialist (Remote)

AuraOne Human Data; United States; Remote; USD 124,000 - 193,000; Full time

Job summary

AuraOne Human Data is seeking a Mathematics Domain Expert for AI Evaluation. This remote contractor role focuses on evaluating AI outputs, reproducing key derivations, and documenting correct methods to guide model training. The work requires strong math background and experience with scholarly sources.

Applicants should have graduate-level math training, publishing or teaching experience, and the ability to work asynchronously at least 10 hours per week from a US-based location.

Qualifications

• Graduate-level training or equivalent applied experience in mathematics or a closely related field.
• Hands-on experience publishing, teaching, or advising on the topic at a professional level.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current mathematics methods, conventions, and prior work for Mathematics Domain Expert — AI Evaluation (Remote, US) assignments.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture the corrected reasoning or worked example so the modeling team can train on it.
• Adjudicate disputed answers against textbooks, papers, or community standards.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Mathematics

Education

Graduate-level training in mathematics

Job description

AuraOne Human Data is seeking a Mathematics Domain Expert for AI Evaluation. This remote contractor role focuses on evaluating AI outputs, reproducing key derivations, and documenting correct methods to guide model training. The work requires strong math background and experience with scholarly sources.

Applicants should have graduate-level math training, publishing or teaching experience, and the ability to work asynchronously at least 10 hours per week from a US-based location.