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# Ground Truth QA Data Specialist

- **Company:** Human Union Data, Inc.
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Auditing
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=56ce37f5fe0213cb

## About the Role

* 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., * 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., * Model output evaluation
 * Rubric-based annotation
 * Severity tagging
 * Inter-rater calibration
 * Ground Truth QA Data evaluation
 * Data quality
 * Taxonomy design
 * Ontology review
 * Ground
 * Truth

Work model

Remote - US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

## 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. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Why this role matters

AI data reviewers help turn ground truth qa data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data., * 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.
 * Document recurring failure modes so the modeling team can target them in the next training run., * 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.


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