Physics PhD Coding Experts

AI Trainer Jobs; United States; Remote; USD 83,000 - 117,000; Full time

Benefits offered by this job

Remote work

Contractor role

Hourly pay

Job summary

AuraOne is seeking remote reviewers with strong physics background to evaluate AI outputs across physics reasoning, calculations, and research workflows. You will grade derivations, reproduce key steps, and document correct methods to train the modeling team.

Applicants should have a PhD or equivalent in physics, plus experience publishing or teaching. The role is contractor-based with flexible, async hours and approximately 10 hours per week minimum.

Qualifications

• Graduate-level physics training or equivalent applied experience.
• Experience publishing, teaching, or advising at a professional level.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning citing methods, papers, or worked examples.
• Available asynchronously for at least 10 hours per week.

Responsibilities

• Review AI outputs against current physics 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 data.
• Adjudicate disputed answers against textbooks, papers, or community standards.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Physics

Education

PhD or equivalent in physics or related field

Job description

Physics PhD Coding Experts 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 / hr · Location: Remote — US-eligible · Contractor

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

Role details

About the role

Physics PhD Coding Experts 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 Coding Experts 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 Coding Experts 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

$70 / 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

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.
- 01 Confirm profile and eligibility details.
- 02 Attach this role deliberately.
- 03 Receive a human review decision or follow-up.
Placement timing depends on program demand and reviewer confirmation.