Preference Dataset QA Preference Data Reviewer

AI Trainer Jobs; United States; Remote; USD 28,000 - 48,000; Part time

Job summary

AuraOne is seeking a Preference Dataset QA Reviewer to remotely evaluate prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and provide structured feedback for model retraining.

As an independent contractor, you will work on a flexible schedule, with hourly compensation. Strong attention to detail, clear written reasoning, and prior annotation or evaluation experience help you succeed.

Qualifications

• Prior evaluation, annotation, or human-rater experience on preference data evaluation.
• Ability to apply rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause.
• Strong attention to detail and accuracy.
• Reliable async 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 rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them for rubric updates.

Skills

Evaluation experience

Annotation experience

Rubric application

Attention to detail

Written reasoning

Async availability

Job description

Preference Dataset QA Preference Data Reviewer is a remote evaluation track for reviewing preference dataset qa 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

Preference Dataset QA Preference Data Reviewer is a remote evaluation track for reviewing preference dataset qa preference data evaluation prompts and responses against AuraOne's quality rubric.

About the role

Preference Dataset QA Preference Data Reviewer is a remote evaluation track for reviewing preference dataset qa 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 preference dataset qa 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 preference dataset qa preference data evaluation model outputs against a versioned rubric and assign severity tags for Preference Dataset QA 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 preference dataset qa preference data evaluation or adjacent content for Preference Dataset QA 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 preference dataset qa 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
• Preference Dataset QA Preference Data evaluation
• Preference ranking
• RLHF
• Rater calibration
• Preference
• Dataset