Ground Truth QA Data Specialist

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

Job summary

AuraOne seeks a Ground Truth QA Data Specialist to evaluate ground truth QA data prompts and responses against our quality rubric. You will compare model outputs, label edge cases, and draft structured feedback for the modeling team to retrain.

Responsibilities include evaluating outputs against a versioned rubric, tagging edge cases and unsafe content, calibrating reviewer scores against gold standards, and providing clear rationales to improve future data quality.

Qualifications

• Prior evaluation, annotation, or human-rater experience on ground truth QA data or adjacent content.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and the rubric clause applied.
• Strong attention to detail and ability to flag prompt issues.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate ground truth QA data 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 to the program team for rubric updates.
• Maintain reviewer-quality scores by calibration against gold-standard examples weekly.

Skills

Evaluation experience

Annotation experience

Inter-rater calibration

Attention to detail

Async availability

Job description

Ground Truth QA Data Specialist is a remote evaluation track for reviewing ground truth qa data evaluation prompts and responses against AuraOne's quality rubric.

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

Ground Truth QA Data Specialist is a remote evaluation track for reviewing ground truth qa data evaluation prompts and responses against AuraOne's quality rubric.

About the role

Ground Truth QA Data Specialist is a remote evaluation track for reviewing ground truth qa data 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 ground truth qa data 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 ground truth qa data evaluation model outputs against a versioned rubric and assign severity tags for Ground Truth QA Data Specialist 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.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

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 ground truth qa data evaluation or adjacent content for Ground Truth QA Data Specialist 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 ground truth qa data 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
• Ground Truth QA Data evaluation
• Data quality
• Taxonomy design
• Ontology review
• Ground
• Truth

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

• - 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.