Mathematics PhD Coding Experts

AI Trainer Jobs; United States; Remote; USD 80,000 - 113,000; Full time

Benefits offered by this job

Remote work

Job summary

Mathematics PhD Coding Experts is a remote review track for evaluating AI outputs across mathematics reasoning, calculations, and research workflows. Reviewers grade derivations and reproduce key results to document correct method so the modeling team can train on it.

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.

Qualifications

• Graduate-level training in mathematics or a closely related field.
• Experience publishing, teaching, or advising on mathematical topics.
• Ability to apply multi-page rubrics consistently across large batches.

Responsibilities

• Review AI outputs against mathematical methods for accuracy.
• Reproduce or sanity-check key derivations and calculations.
• Flag errors with structured severity tags.
• Document corrected reasoning and worked examples for training data.

Skills

Scientific reasoning

Citation review

Method validation

Quantitative analysis

Multilingual fluency

Education

PhD or equivalent in mathematics or related field

Job description

Role details

Mathematics PhD Coding Experts is a remote review track for evaluating AI outputs across mathematics 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

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

About the role

Mathematics PhD Coding Experts is a remote review track for evaluating AI outputs across mathematics 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.
Mathematics 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 mathematics methods, conventions, and prior work for Mathematics 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.

What you should bring

• Graduate-level training or equivalent applied experience in mathematics or a closely related field for Mathematics 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.

Example tasks

• Reproduce a mathematics 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
• Mathematics