Remote Life Sciences Research Review Specialist

AuraOne; United States; On-site; USD 34,000 - 55,000; Full time

Job summary

AuraOne seeks a Life Sciences Research Assistant to remotely evaluate AI outputs across life sciences research duties. You will review derivations, reproduce key steps, and document methods to train the modeling team. This is a contractor role with US eligibility and requires reliable weekly availability.

Ideal candidates have graduate-level training or equivalent, hands-on publishing or teaching experience, and strong written reasoning citing methods and papers. Remote work is supported.

Qualifications

• Graduate-level training or equivalent applied experience in life sciences research review.
• 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 life sciences research review methods, conventions, and prior work.
• 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

Life Sciences Review

Education

Graduate-level training

PhD or related field

Job description

AuraOne seeks a Life Sciences Research Assistant to remotely evaluate AI outputs across life sciences research duties. You will review derivations, reproduce key steps, and document methods to train the modeling team. This is a contractor role with US eligibility and requires reliable weekly availability.

Ideal candidates have graduate-level training or equivalent, hands-on publishing or teaching experience, and strong written reasoning citing methods and papers. Remote work is supported.