Physics PhD Researchers

AI Trainer Jobs; United States; Remote; USD 96,000 - 124,000; Full time

Job summary

AuraOne is seeking Physics PhD Researchers for a remote contractor role to review AI outputs across physics reasoning and research workflows. Reviewers assess derivations, reproduce key results, and document the correct method for training the modeling team.

Ideal candidates hold graduate-level physics credentials with experience publishing or teaching, and can commit at least 10 hours weekly remotely from the US.

Qualifications

• Graduate-level training or equivalent in physics or related field.
• 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 physics methods and prior work.
• Reproduce or sanity-check key derivations and calculations.
• Flag dimensional, methodological, and citation errors with structured notes.
• Capture corrected reasoning or worked examples for training.
• Adjudicate disputed answers against textbooks, papers, or standards.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Physics

Education

Graduate-level physics degree or equivalent

Job description

Physics PhD Researchers 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: $70–$90 / hr · Location: Remote — US-eligible · Contractor

Physics PhD Researchers is a remote review track for evaluating AI outputs across physics reasoning, calculations, and research workflows.

Role details

About the role

Physics PhD Researchers 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 PhD Researchers 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.

What you should bring

• Graduate-level training or equivalent applied experience in physics or a closely related field for Physics PhD Researchers 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

$70–$90 / hr
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