Physics Expert is a remote review track for evaluating AI outputs across physics reasoning, calculations, and research workflows.
ContractorRemote — US-eligible$60–$85 / hr
Static snapshot. Listings are generated from bundled local jobs data last refreshed on July 1, 2026. Confirm availability through AuraOne intake before relying on a role.
Physics Expert is a remote review track for evaluating AI outputs across physics 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.
Physics 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 physics methods, conventions, and prior work for Physics 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.
Adjudicate disputed answers against textbooks, papers, or community standards.
Maintain reviewer-quality scores in inter-rater calibration cycles.
What you should bring
Graduate-level training or equivalent applied experience in physics or a closely related field for Physics 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.
Role signals
Example tasks
Reproduce a physics 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
$60–$85 / hr
Expected arrangement: contractor, with program-defined task volume and review pacing. A snapshot does not guarantee current placement availability.
Skills used in matching
Scientific reasoning
Method validation
Citation review
Quantitative analysis
Physics
analytical reasoning
collaboration
continuous learning
data analysis tools
educational content development
physics expertise
remote work proficiency
Application boundary
Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role snapshot and its source to your candidate record.
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