Dataset Deduplication Ontology QA Reviewer

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Full time

Job summary

AI Trainer Jobs seeks a Dataset Deduplication Ontology QA Reviewer for remote evaluation work reviewing prompts and responses against AuraOne's quality rubric. You will compare outputs, label edge cases, and write structured feedback to help retrain models.

The contractor role requires prior evaluation or annotation experience, clear written reasoning, and reliable async availability for at least 10 hours per week.

Qualifications

• Prior evaluation, annotation, or human-rater experience on dataset deduplication ontology qa evaluation or adjacent content.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate dataset deduplication ontology qa evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.

Skills

Evaluation experience

Written reasoning

Attention to detail

Async availability

Job description

Dataset Deduplication Ontology QA Reviewer is a remote evaluation track for reviewing dataset deduplication ontology qa evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Category: Data Quality, Taxonomy & Ontology · Pay: $80–$120 / hr · Location: Remote — US-eligible · Contractor

Dataset Deduplication Ontology QA Reviewer is a remote evaluation track for reviewing dataset deduplication ontology qa evaluation prompts and responses against AuraOne's quality rubric.

About the role

Dataset Deduplication Ontology QA Reviewer is a remote evaluation track for reviewing dataset deduplication ontology qa evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn dataset deduplication ontology qa evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Design and audit label taxonomies, knowledge graphs, and evaluation datasets.

Responsibilities

• Evaluate dataset deduplication ontology qa evaluation model outputs against a versioned rubric and assign severity tags for Dataset Deduplication Ontology QA Reviewer assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on dataset deduplication ontology qa evaluation or adjacent content for Dataset Deduplication Ontology QA Reviewer work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Role signals

Example tasks

• Compare two dataset deduplication ontology qa evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

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

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Dataset Deduplication Ontology QA evaluation
• Data quality
• Taxonomy design
• Ontology review
• Dataset
• Deduplication

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.