Math Expert (PhD)

AI Trainer Jobs; United States; Remote; USD 58,000 - 80,000; Part time

Job summary

AuraOne is seeking a Math Expert (PhD) for a remote review track to evaluate AI outputs in math research, reasoning, calculations, and workflows. Reviewers assess derivations, reproduce key steps, and document the correct method for training the modeling team.

Responsibilities include grading derivations, reproducing results, and highlighting errors with a structured rubric. The role is contractor-based with flexible, async availability and an emphasis on precise mathematical communication.

Qualifications

• Graduate-level training or equivalent in math research review or related field.
• Hands-on 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.

Responsibilities

• Review AI outputs against math research review methods and prior work.
• Reproduce or sanity-check key derivations, calculations, or claims.
• Flag errors with structured severity tags.
• Capture corrected reasoning or worked examples for training.

Skills

Math reasoning

Research review

Academic publishing

Clear written reasoning

Education

PhD in Mathematics

Tools

Rubrics

Job description

Math Expert (PhD) is a remote review track for evaluating AI outputs across math research review 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: Math, Reasoning & Formal Methods · Pay: $50 / hr · Location: Remote — US-eligible · Contractor

Math Expert (PhD) is a remote review track for evaluating AI outputs across math research review reasoning, calculations, and research workflows.

Role details

About the role

Math Expert (PhD) is a remote review track for evaluating AI outputs across math research review 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.

Math research review 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.

Judge mathematical reasoning and theorem proving. Long proof chains included.

Responsibilities

• Review AI outputs against current math research review methods, conventions, and prior work for Math Expert (PhD) 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 math research review or a closely related field for Math Expert (PhD) 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 math research review 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
• Math research review

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.