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# Physics PhD Coding Expert - AI Code and Scientific Computing Evaluation

- **Company:** AuraOne Human Data
- **Location:** United States (Remote available)
- **Salary:** $145,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Scientific Computating, Model Validation
- **Published:** September 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d5b969c4d413280c

## About the Role

* Graduate-level training or equivalent applied experience in physics or a closely related field for Physics PhD Coding Expert - AI Code and Scientific Computing Evaluation 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., * 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., * Scientific reasoning
 * Method validation
 * Citation review
 * Quantitative analysis
 * Physics

Work model

Remote - US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

## Description

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., * Review AI outputs against current physics methods, conventions, and prior work for Physics PhD Coding Expert - AI Code and Scientific Computing Evaluation 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., * 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.


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