Language & Audio Expert

AI Trainer Jobs; United States; Remote; USD 48,000 - 69,000; Full time

Job summary

AuraOne invites applicants to join its Language & Audio Expert program. This remote contractor role focuses on evaluating audio annotation prompts and responses against a structured rubric. Reviewers assess model outputs, label edge cases, and generate feedback to retrain the system.

You will judge voice agents and pronunciation quality, contributing to robust data labeling practices. Ideal candidates bring linguistics or content-moderation background and experience applying rubrics across long

Qualifications

• Experience evaluating audio annotation or adjacent content.
• Ability to apply multi-page rubrics consistently.
• Clear written reasoning assigning issues to rubric clauses.

Responsibilities

• Evaluate audio annotation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route to rubric updates.
• Calibrate reviewer scores against gold-standard examples weekly.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Audio annotation

Job description

Language & 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: $35–$50 / hr · Location: Remote — US-eligible · Contractor

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

Role details

About the role

Language & 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 Language & 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 Language & 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.

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

$35–$50 / hr
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.