Rater Calibration Preference Data Reviewer

AuraOne, Inc.; United States; Remote; USD 34,000 - 55,000; Full time

Job summary

AuraOne, Inc. seeks a remote Rater Calibration Preference Data Reviewer to evaluate data evaluation prompts and responses against AuraOne's quality rubric. You will compare paired outputs, label edge cases, and provide structured feedback that helps retrain the model.

This contractor role requires strong attention to detail, ability to apply rubrics across long batches, and clear written reasoning. You will work asynchronously for at least 10 hours weekly from the US, with compensation discussed

Qualifications

• Prior evaluation, annotation, or human-rater experience on data evaluation tasks.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning citing rubric clauses.
• Reliable asynchronous availability for at least 10 hours per week.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Document recurring failure modes to guide future rubrics.

Skills

Model evaluation

Rubric annotation

Severity tagging

Inter-rater calibration

Rater Calibration Data evaluation

Preference ranking

RLHF

Job description

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

Responsibilities

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

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.