Cell Biologist

AI Trainer Jobs; United States; Remote; USD 55,000 - 83,000; Full time

Job summary

AI Trainer Jobs is seeking a Cell Biologist reviewer to evaluate AI outputs in geospatial science reasoning. You will reproduce key derivations, sanity-check calculations, and document the correct methods for training.

This remote contractor role requires graduate-level training in geospatial science, strong written reasoning, and ability to apply rubrics across long batches. Expect ~10 hours/week, program-defined task volume.

Qualifications

• Graduate-level training or equivalent applied experience in geospatial science or a closely related field for Cell Biologist 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 geospatial science methods, conventions, and prior work for Cell Biologist 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.

Skills

Geospatial science

Scientific reasoning

Method validation

Citation review

Multilingual fluency

Quantitative analysis

Education

Graduate-level geospatial science background

Job description

About the role

Cell Biologist is a remote review track for evaluating AI outputs across geospatial science 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.
Geospatial science 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 geospatial science methods, conventions, and prior work for Cell Biologist 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.

What you should bring

• Graduate-level training or equivalent applied experience in geospatial science or a closely related field for Cell Biologist 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 geospatial science 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

Hourly rate confirmed after the interview process.
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 reviewQuantitative analysis
• Geospatial science