Audio Expert

AI Trainer Jobs; United States; Remote; USD 55,000 - 83,000; Part time

Job summary

AuraOne is seeking an Audio Expert for a remote evaluation track. You will review audio annotation prompts and responses against AuraOne's quality rubric, label edge cases, and provide structured feedback to retrain models.

You will judge voice agents and speech recognition, including pronunciation and conversation quality. The role involves evaluating model outputs, tagging issues with a rubric-based system, and maintaining calibration against gold standards on a weekly basis.

Qualifications

• Prior evaluation, annotation, or human-rater experience on audio annotation or adjacent content for Audio Expert work.

Responsibilities

• Evaluate audio annotation model outputs against a versioned rubric and assign severity tags for Audio Expert 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.

Skills

Audio evaluation

Annotation review

Rubric tagging

Inter-rater calibration

Written reasoning

Attention to detail

Async availability

Job description

Audio Expert 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

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

Role details

About the role

Audio Expert 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 Audio Expert 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on audio annotation or adjacent content for Audio Expert 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
• Audio annotation

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• - 01 Confirm profile and eligibility details.
• - 02 Attach this role deliberately.
• - 03 Receive a human review decision or follow-up.

Placement timing depends on program demand and reviewer confirmation.