Labeler Consensus Preference Data Reviewer

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Full time

Job summary

AuraOne is seeking a Labeler Consensus Preference Data Reviewer for a remote evaluation track. You will review labeler consensus preference data evaluation prompts and responses against our quality rubric, compare paired outputs, and write structured feedback the modeling team can use to retrain.

You will produce preference rankings, reward-model feedback, and calibrated human judgment for post-training pipelines, tagging edge cases and risk signals, while ensuring consistency across long

Qualifications

• Prior evaluation, annotation, or human-rater experience on labeler consensus data evaluation or adjacent content.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.

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.

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.

Category: RLHF & Human Preference Data · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

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.

About the role

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.
AI data reviewers help turn labeler consensus preference data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Produce preference rankings, reward-model feedback, and calibrated human judgment for post-training pipelines.

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.

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 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.

Role signals

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.

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
• Labeler Consensus Preference Data evaluation
• Preference ranking
• RLHF
• Rater calibration
• Labeler
• Consensus

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

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• - 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.