Podcast Transcript Audio QA Reviewer.

Podcast Transcript Audio QA Reviewer is a remote evaluation track for reviewing audio annotation 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 audio annotation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.

Evaluate voice agents, speech recognition, audio events, pronunciation, conversation quality, and spoken interaction.

• ↳Evaluate audio annotation model outputs against a versioned rubric and assign severity tags for Podcast Transcript Audio QA 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.

• ↳Prior evaluation, annotation, or human-rater experience on audio annotation or adjacent content for Podcast Transcript Audio QA 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.

Hourly rate confirmed after the interview process.

Expected schedule: contractor, remote specialist work with program-defined task volume and review pacing.

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Audio annotation
• Speech evaluation
• Voice QA
• Audio review
• Podcast
• Transcript

AuraOne uses a shared specialist intake to confirm track fit, review readiness, and the best queue for your profile. Applications submitted from partner job boards carry the source, role, and category on the apply URL.