Aerospace AI Review Expert (Remote, Part‑Time)

AuraOne Human Data; United States; Remote; USD 110,000 - 255,000; Part time

Job summary

AuraOne, Inc. seeks an Aerospace Engineering Expert for AI Technical Review. This remote contractor role involves evaluating model outputs, reproducing key derivations, and flagging errors to improve training data.

It requires graduate-level aerospace training and strong written reasoning, with at least 10 hours per week availability. Successful candidates will review methods against textbooks and papers, adjudicate disputed results, and maintain calibration scores across batches.

Qualifications

• Graduate-level training or equivalent applied experience in aerospace engineering or a closely related field for Aerospace Engineering Expert — AI Technical Review work.
• 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 aerospace engineering methods, conventions, and prior work for Aerospace Engineering Expert — AI Technical Review 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

Aerospace engineering

Education

Graduate-level training in aerospace engineering

Job description

AuraOne, Inc. seeks an Aerospace Engineering Expert for AI Technical Review. This remote contractor role involves evaluating model outputs, reproducing key derivations, and flagging errors to improve training data.

It requires graduate-level aerospace training and strong written reasoning, with at least 10 hours per week availability. Successful candidates will review methods against textbooks and papers, adjudicate disputed results, and maintain calibration scores across batches.