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# Labeler Consensus Preference Data Reviewer

- **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=8e4704ecbf433987

## About the Role

* Prior evaluation, annotation, or human-rater experience on labeler consensus preference data evaluation or adjacent content for Labeler Consensus Preference Data 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., * 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
 * Labeler Consensus Preference Data evaluation
 * Preference ranking
 * RLHF
 * Rater calibration
 * Labeler
 * Consensus

Work model

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

## Description

Labeler Consensus Preference Data Reviewer is a remote evaluation track for reviewing labeler consensus preference 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 labeler consensus preference data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data., * Evaluate labeler consensus preference data evaluation model outputs against a versioned rubric and assign severity tags for Labeler Consensus Preference Data 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.
 * 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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