Meeting Audio Conversation Evaluator

AI Trainer Jobs; United States; Remote; USD 57,000 - 81,000; Full time

Job summary

AuraOne is seeking a Meeting Audio Conversation Evaluator for a remote contractor role. You will review audio annotation prompts and responses, label edge cases, and provide structured feedback to help retrain the model.

Evaluate outputs against a quality rubric and document your findings with clear rationale. Responsibilities include comparing outputs, tagging issues, and calibrating against gold standards weekly.

Qualifications

• Experience in audio annotation or human data review.
• Ability to apply multi-page rubrics consistently.
• Strong written reasoning and clear issue naming.
• Reliable 10+ hours per week availability.

Responsibilities

• Evaluate audio annotation model outputs against a rubric and tag severity.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and suggest rubric updates to the team.
• Maintain reviewer-quality scores by calibrating against gold-standard examples.

Skills

Audio annotation experience

Attention to detail

Written reasoning

Remote/async work

Job description

Meeting Audio Conversation Evaluator 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.

Category: Voice, Audio & Speech AI · Pay: $50 / hr · Location: Remote — US-eligible · Contractor

Meeting Audio Conversation Evaluator is a remote evaluation track for reviewing audio annotation prompts and responses against AuraOne's quality rubric.

About the role

Meeting Audio Conversation Evaluator 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.
Judge voice agents and speech recognition. Pronunciation and conversation quality included.

Responsibilities

• Evaluate audio annotation model outputs against a versioned rubric and assign severity tags for Meeting Audio Conversation 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 audio annotation or adjacent content for Meeting Audio Conversation 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 audio annotation 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 ​