Biology / environmental science Evaluator

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Part time

Job summary

AuraOne is seeking a Biology / environmental science Evaluator to remotely review AI outputs, reproduce key steps, and document correct methods for training the modeling team. This independent contractor role emphasizes scientific rigor and reproducibility across biology reasoning and data-driven workflows.

Qualified candidates bring PhD/postdoc or equivalent research experience, strong written reasoning, and the ability to work asynchronously for at least 10 hours/week.

Qualifications

• Graduate-level training or equivalent in biology or related field.
• Publish/teach/advising experience at professional level is preferred.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning citing methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current biology methods and prior work for evaluators.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture corrected reasoning or worked examples for model training.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Biology

Education

PhD / postdoc / industry research experience

Job description

About the role

Biology / environmental science Evaluator is a remote review track for evaluating AI outputs across biology reasoning, calculations, and research workflows. Reviewers grade derivations and assumptions, reproduce key results, and document the correct method so the modeling team can train on it.
Biology models live or die on whether their derivations actually hold up under scrutiny. AuraOne uses scientific specialists to grade outputs the way a peer reviewer would — checking assumptions, reproducing key steps, and capturing the right method alongside the wrong one.
Bring scientific and technical domain expertise into AI reasoning, research, and dataset review.

Responsibilities

• Review AI outputs against current biology methods, conventions, and prior work for Biology / environmental science Evaluator 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.

Role details

Track STEM research review Work model Remote · Independent specialist contractor Compensation $80–$120 / hr Eligible from US

What you should bring

• Graduate-level training or equivalent applied experience in biology or a closely related field for Biology / environmental science Evaluator 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.

Role signals

Example tasks

• Reproduce a biology derivation from a model output and flag any algebraic or dimensional errors.
• Grade a model's literature summary against the cited papers and rate the citation quality.
• Adjudicate a disputed answer between two reviewers using textbook methods.
• Audit a 25-row batch for rubric consistency and report drift to the program lead.

Useful experience

• PhD, postdoc, or industry research experience in the topic area.
• Prior work reviewing AI-assisted research tooling and its failure modes.
• Multilingual fluency for non-English papers and corpora.

Compensation and schedule

$80–$120 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Scientific reasoning
• Method validation
• Citation review
• Quantitative analysis
• Biology