Cell/Molecular Biologist (Stem Cells)

AI Trainer Jobs; United States; Remote; USD 141,000 - 190,000; Full time

Benefits offered by this job

Remote work

Contractor engagement

Job summary

AI Trainer Jobs is seeking a Cell/Molecular Biologist (Stem Cells) for a remote review track, evaluating AI outputs across geospatial science reasoning, calculations, and research workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

Geospatial models require rigorous validation; PhD or equivalent research experience, multilingual capabilities, and strong written reasoning are preferred.

Qualifications

• PhD, postdoc, or industry research experience in geospatial science or related field.
• Experience reviewing AI-assisted research tooling and its failure modes.
• Multilingual fluency for non-English papers and corpora.

Responsibilities

• Review AI outputs against geospatial science methods and prior work.
• Reproduce or sanity-check key derivations and claims.
• Flag dimensional, methodological, and citation errors with severity tags.
• Capture corrected reasoning to train the modeling team.

Skills

Geospatial science

Scientific reasoning

Multilingual fluency

Clear written reasoning

Data analysis

Remote collaboration

Attention to detail

Education

PhD or equivalent research experience

Job description

Cell/Molecular Biologist (Stem Cells) 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.

Category: Scientific AI & Domain Experts · Pay: $120 / hr · Location: Remote — US-eligible · Contractor

Cell/Molecular Biologist (Stem Cells) is a remote review track for evaluating AI outputs across geospatial science reasoning, calculations, and research workflows.

About the role

Cell/Molecular Biologist (Stem Cells) 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/Molecular Biologist (Stem Cells) 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 Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Graduate-level training or equivalent applied experience in geospatial science or a closely related field for Cell/Molecular Biologist (Stem Cells) 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 review
• Quantitative analysis
• Geospatial science