The work
As an Audio Quality AI Data Reviewer, you will review and annotate audio and robotic video content for AI training. You will assess recording clarity, identify specific problems, and provide accurate labels, notes, and feedback using project guidelines.
The work suits people with audio production knowledge who can apply quality standards consistently across structured media-review tasks.
- Review and annotate audio and robotic video data.
- Identify background noise, clipping, distortion, echo, hum, dropouts, and other recording problems.
- Assess audio quality and clarity, then categorize issues accurately.
- Write concise labels, notes, and feedback for reviewed content.
- Follow annotation standards and maintain accuracy across assigned tasks.
- Communicate findings clearly while working independently in a remote setting.
What it pays and takes
This is a part-time contractor role for English-speaking applicants. Previous data annotation or AI training experience is helpful but not required.
- Pay: $50-$90 USD per hour.
- Time: 20+ hours per week.
- Work arrangement: Remote contract work.
- Language: Strong written and verbal English.
- Location: Open to applicants in the countries listed for this role.
- Experience: Professional or academic experience in audio engineering, audio production, sound editing, or a related field.
- Skills: Strong knowledge of audio quality and common recording issues.
- Tools: Familiarity with audio editing or audio production tools.
- Requirements: Careful attention to detail, comfort following structured guidelines, and ability to review video and audio content.
- Helpful background: A bachelor's degree in audio engineering, sound production, music technology, or a related field.
- Helpful experience: Audio quality assurance, editing, annotation, or media review.
About AI training work
AI training work uses human reviews, labels, and feedback to help improve artificial intelligence systems. Audio reviewers are paid for applying careful listening and production knowledge to the examples used to train and evaluate these systems.
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