Remote Mechanical Engineering AI Review Specialist

AuraOne; United States; À distance; USD 138 000 - 165 000; Temps partiel

Résumé du poste

AuraOne is seeking Mechanical Engineering Experts for a remote review track to evaluate AI outputs in mechanical engineering reasoning, calculations, and research workflows. Reviewers reproduce results, check methods, and document the correct approach so the modeling team can train on it.

The role involves grading derivations, flagging errors with structured severity, and maintaining inter-rater calibration scores.

Qualifications

• Graduate-level training or equivalent in mechanical engineering.
• Experience publishing, teaching, or advising on the topic at a professional level.
• Ability to apply 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.

Responsabilités

• Review AI outputs against current mechanical engineering methods, conventions, and prior work for Mechanical Engineering Experts 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.

Connaissances

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Mechanical engineering

Formation

Graduate degree in mechanical engineering or related field

Description du poste

AuraOne is seeking Mechanical Engineering Experts for a remote review track to evaluate AI outputs in mechanical engineering reasoning, calculations, and research workflows. Reviewers reproduce results, check methods, and document the correct approach so the modeling team can train on it.

The role involves grading derivations, flagging errors with structured severity, and maintaining inter-rater calibration scores.