Post-Training Data Preference Data Reviewer

Post-Training Data Preference Data Reviewer is a remote evaluation track for reviewing post training data 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 post training data preference data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data. Responsibilities Evaluate post training data preference data evaluation model outputs against a versioned rubric and assign severity tags for Post-Training Data 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 post training data preference data evaluation or adjacent content for Post-Training Data 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 post training data 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 Post Training Data Preference Data evaluation Preference ranking RLHF Rater calibration Post Training Data 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.