Coq Formalization Model Evaluator

AI Trainer Jobs; United States; Remote; USD 55,000 - 83,000; Full time

Job summary

AI Trainer Jobs seeks a Coq Formalization Model Evaluator to remotely review AI outputs in Coq formalization research. You will grade derivations, reproduce key results, and document methods to train modeling teams.

The work emphasizes rigorous reasoning and citation accuracy, with an hourly contractor arrangement. Responsibilities include verifying calculations, applying long-form rubrics, and ensuring reproducibility of results.

Qualifications

• Graduate-level or higher research experience in formal methods or related field.
• Experience publishing, teaching, or advising on the topic at a professional level.
• Ability to apply rubrics consistently across long review batches.

Responsibilities

• Review AI outputs against Coq formalization methodologies and prior work.
• Reproduce or sanity-check key derivations and calculations.
• Flag errors with structured severity tags.
• Capture corrected reasoning to train models.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Coq formalization

Formal reasoning

Proof review

Formalization

Education

PhD or equivalent research experience

Job description

Coq Formalization Model Evaluator is a remote review track for evaluating AI outputs across coq formalization model 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

Coq Formalization Model Evaluator is a remote review track for evaluating AI outputs across coq formalization model research review reasoning, calculations, and research workflows.

About the role

Coq Formalization Model Evaluator is a remote review track for evaluating AI outputs across coq formalization model 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.
Coq Formalization Model 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 coq formalization model research review methods, conventions, and prior work for Coq Formalization Model Evaluator 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.

Role details

Track STEM research review Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Graduate-level training or equivalent applied experience in coq formalization model research review or a closely related field for Coq Formalization Model Evaluator 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 coq formalization model 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
• Coq Formalization Model research review
• Formal reasoning
• Proof review
• Formalization