Data-heavy chemistry/materials Science Expert

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

Job summary

AuraOne seeks a Data-heavy chemistry/materials Science Expert to remotely review AI outputs in materials science reasoning, calculations, and research workflows. Reviewers validate derivations, reproduce results, and document correct methods to train the modeling team.

Experience with grading outputs, applying rubrics, and ensuring reproducibility is essential. This contractor role requires independence and the ability to commit time weekly for review tasks.

Qualifications

• Graduate-level training or equivalent in materials science or related field.
• Hands-on experience publishing, teaching, or advising on the topic at a professional level.
• Clear written reasoning citing methods, papers, or worked examples.

Responsibilities

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

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Materials science

Education

PhD

Postdoc

Industry research experience

Job description

Data-heavy chemistry/materials Science Expert is a remote review track for evaluating AI outputs across materials 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

Data-heavy chemistry/materials Science Expert is a remote review track for evaluating AI outputs across materials science reasoning, calculations, and research workflows.

About the role

Data-heavy chemistry/materials Science Expert is a remote review track for evaluating AI outputs across materials 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.
Materials 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 materials science methods, conventions, and prior work for Data-heavy chemistry/materials Science Expert 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 materials science or a closely related field for Data-heavy chemistry/materials Science Expert 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.

Example tasks

• Reproduce a materials 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
• Materials science