Evaluation Set Curation Ontology QA Reviewer

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

Job summary

AI Trainer Jobs is seeking a remote Evaluation Set Curation Ontology QA Reviewer to help review prompts and responses against AuraOne's quality rubric. This contractor role involves comparing outputs, labeling edge cases, and providing structured feedback the modeling team can use to retrain.

Responsibilities include evaluating outputs, marking edge cases, and ensuring rubric alignment. The role requires strong written reasoning, attention to detail, and at least 10 hours per week of

Qualifications

• Prior evaluation, annotation, or human-rater experience on evaluation set curation 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 outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger with rationale.
• Label hallucinations, instruction-following failures, and unsafe content.
• Capture ambiguous prompts and route to program team.

Skills

Annotation

Inter-rater calibration

Attention to detail

Job description

Evaluation Set Curation Ontology QA Reviewer is a remote evaluation track for reviewing evaluation set curation 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

Evaluation Set Curation Ontology QA Reviewer is a remote evaluation track for reviewing evaluation set curation ontology qa evaluation prompts and responses against AuraOne's quality rubric.

About the role

Evaluation Set Curation Ontology QA Reviewer is a remote evaluation track for reviewing evaluation set curation 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 evaluation set curation 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 evaluation set curation ontology qa evaluation model outputs against a versioned rubric and assign severity tags for Evaluation Set Curation 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 evaluation set curation ontology qa evaluation or adjacent content for Evaluation Set Curation 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.

Example tasks

• Compare two evaluation set curation 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
• Evaluation Set Curation Ontology QA evaluation
• Data quality
• Taxonomy design
• Ontology review
• Evaluation
• Curation

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.