Audio Quality AI Data Reviewer

You will review and annotate audio and robotic video data for AI training. Using project guidelines, you will assess recording quality, identify specific problems, and provide accurate labels, notes, and feedback.

You will work independently in a remote setting while applying annotation standards consistently across assigned tasks.

• Review audio and robotic video content for clarity and overall quality.
• Identify background noise, clipping, distortion, echo, hum, dropouts, and other recording issues.
• Categorize audio problems accurately using defined guidelines.
• Write concise labels, notes, and feedback about reviewed content.
• Communicate findings clearly and maintain accuracy across tasks.

What it pays and takes

This is an entry-level, part-time contractor role. Previous data annotation or AI training experience can help, but it is not required when you have the relevant audio background.

• Pay: $50 to $90 USD per hour.
• Time: 20 or more hours per week.
• Work arrangement: Remote.
• Language: Strong written and verbal fluency in English.
• Location: Open to candidates in AE, AR, AT, BD, BE, BH, BR, CA, CH, CL, CO, DE, DK, DZ, EG, ES, ET, FR, GB, GH, GR, ID, IN, IT, JO, JP, KE, KG, KW, KZ, LB, LK, MA, MX, MY, NG, NL, OM, PE, PH, PK, PS, PT, QA, SA, SG, TH, TN, TW, US, UZ, VN, and ZA.
• Experience: Professional or academic experience in audio engineering, audio production, sound editing, or a related field.
• Skills: Strong understanding of audio quality and common recording problems; familiarity with audio editing or production tools; attention to detail; and the ability to follow structured guidelines.
• Helpful background: A bachelor's degree in Audio Engineering, Sound Production, Music Technology, or a related field, plus experience in audio quality assurance, editing, annotation, or media review.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind artificial intelligence, including reviewing recordings and preparing clear, consistent labels and feedback. People with audio expertise are paid to judge quality that automated systems may not reliably recognize.