Physical Affordance Failure Case Reviewer

AI Trainer Jobs; United States; Remote; USD 136,000 - 194,000; Part time

Job summary

AuraOne is seeking a Physical Affordance Failure Case Reviewer for its remote review track evaluating AI outputs in physics reasoning, calculations, and research workflows. Reviewers grade derivations and document correct methods to train the modeling team.

This contractor role requires graduate-level physics expertise, meticulous rubric application, and the ability to work asynchronously about 10 hours per week. Compensation is hourly with US eligibility.

Qualifications

• Graduate-level training or equivalent applied experience in physics or a closely related field.
• Experience publishing, teaching, or advising on the topic at a professional level.
• Ability to apply 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 current physics methods, conventions, and prior work for Physical Affordance Failure Case Reviewer 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

Physics reasoning

Quantitative analysis

Research experience

Technical documentation

Multilingual

Education

PhD, postdoc, or industry research experience in physics

Job description

Physical Affordance Failure Case Reviewer

Physical Affordance Failure Case Reviewer 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.

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

About the role

Physical Affordance Failure Case Reviewer 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 Physical Affordance Failure Case Reviewer 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 physics or a closely related field for Physical Affordance Failure Case Reviewer 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 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

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
• Physics
• Robotics evaluation
• Embodied AI
• Safety review
• Physical
• Affordance