Deep Research Task Evaluator

AI Trainer Jobs; United States; Remote; USD 141,000 - 190,000; Part time

Benefits offered by this job

Hourly pay

Job summary

AuraOne is seeking a Deep Research Task Evaluator to remotely assess AI outputs across advanced research tasks, reasoning, and data workflows. Reviewers verify derivations, reproduce key steps, and document correct methods for training data generation.

The role emphasizes rigorous evaluation, precise citation checks, and clear written reasoning. Candidates should have graduate-level training or equivalent applied experience, strong async availability, and multilingual capabilities for

Qualifications

• Graduate-level training or equivalent applied experience in deep research task review.
• Clear written reasoning citing methods, papers, or worked examples.
• Multilingual fluency for non-English papers and corpora.

Responsibilities

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

Skills

Deep research review

Graduate-level training

Written reasoning

Async availability

Multilingual fluency

Education

PhD/postdoc/industry research

Tools

Browser automation

Web research

Job description

Deep Research Task Evaluator is a remote review track for evaluating AI outputs across deep research task 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: Scientific AI & Domain Experts · Pay: $120 / hr · Location: Remote — US-eligible · Contractor

Deep Research Task Evaluator is a remote review track for evaluating AI outputs across deep research task research review reasoning, calculations, and research workflows.

About the role

Deep Research Task Evaluator is a remote review track for evaluating AI outputs across deep research task 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.
Deep Research Task 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.
Bring scientific and technical domain expertise into AI reasoning, research, and dataset review.

Responsibilities

• Review AI outputs against current deep research task research review methods, conventions, and prior work for Deep Research Task 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 deep research task research review or a closely related field for Deep Research Task 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.

Example tasks

• Reproduce a deep research task 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
• Deep Research Task research review
• Web research
• Source grounding
• Browser automation
• Deep
• Research

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.