Pairwise Preference Preference Data Reviewer

AuraOne, Inc.; United States; Remote; USD 28,000 - 55,000; Full time

Job summary

AuraOne, Inc. is seeking a remote Data Reviewer to assess pairwise preference data against our quality rubric. You will compare outputs, label edge cases, and generate structured feedback for retraining the model.

The role requires clear written reasoning, strong attention to detail, and reliable availability for at least 10 hours per week. This is a CONTRACTOR position with remote access for US applicants.

Qualifications

• Prior evaluation, annotation, or human-rater experience on pairwise preference data evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate pairwise preference data evaluation model outputs against a versioned rubric and assign severity tags.
• 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.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Pairwise Preference Preference Data"

Preference ranking

RLHF

Rater calibration

Pairwise

Preference

Job description

Pairwise Preference Preference Data Reviewer is a remote evaluation track for reviewing pairwise preference 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 pairwise preference preference data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.

Responsibilities

• Evaluate pairwise preference preference data evaluation model outputs against a versioned rubric and assign severity tags for Pairwise Preference 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 pairwise preference preference data evaluation or adjacent content for Pairwise Preference 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 pairwise preference 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
• Pairwise Preference Preference Data evaluation
• Preference ranking
• RLHF
• Rater calibration
• Pairwise
• Preference

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.

Prior evaluation, annotation, or human-rater experience on pairwise preference preference data evaluation or adjacent content for Pairwise Preference 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.