Conversational AI Evaluator

About the role Conversational AI Evaluator is a remote evaluation track for reviewing conversational ai 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 conversational ai evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data. Review frontier model outputs. Judge benchmark failures and calibrate other evaluators. Responsibilities Evaluate conversational ai evaluation model outputs against a versioned rubric and assign severity tags for Conversational AI Evaluator 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. 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 conversational ai evaluation or adjacent content for Conversational AI Evaluator 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 conversational ai 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 Conversational AI evaluation Voice, language and multimodal AI evaluation Rubric writing Expert review #J-18808-Ljbffr AI Trainer Jobs