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Labeler Consensus Preference Data Reviewer

AuraOne Human Data
Remote

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Job Details

Qualifications

Model evaluation

Full Job 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.

Responsibilities

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.

Qualifications

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.

Example tasks

Compare two labeler consensus preference 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.

Nice to have

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.

Skills

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.

Compensation

Hourly rate confirmed after the interview process.

Application process

Apply through AuraOne's specialist intake for role-specific routing and review. Final project scope, schedule, and contractor terms are confirmed before placement.

Apply Now

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data labeler jobs in remote (

Labeler Consensus Preference Data Reviewer

AI Response Labeler / Annotator – Castilian Spanish Specialty

Labeler Consensus Reward Model Evaluator

Senior Manager, Machine Learning (Data Operations)

Medicaid Rebate Analyst Services Specialist

Senior CQV Engineer / Validation Lead

Technical Senior Sales Manager

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