Data annotation job
Audiobook QA Expert, Italian
Mercor · Posted 1 month ago
- Data annotation
- AI evaluation
- Multilingual
- Audio
About this role
Help make the next generation of AI-narrated audiobooks sound genuinely human.
1. Overview
We're building a team of native Italian speakers who love audiobooks to help evaluate AI-narrated (text-to-speech) audiobooks in Italian (Italy). You'll listen the way a real audiobook fan does, but with a careful, analytical ear, and flag the moments where the narration slips, so the underlying models can be made better.
This is remote, part-time work at roughly 20 hours per week (about 4 hours per day). You can be based anywhere, though we prefer people living in a region where Italian is natively spoken.
2. What You'll Do
- Listen to AI-narrated audiobooks in Italian and assess how natural, accurate, and enjoyable the narration is.
- Spot and log errors in the synthesized speech, skipped, added, or mispronounced words; numbers read incorrectly; abbreviations expanded wrongly; unnatural phrasing or intonation; audio glitches and artifacts.
- Categorize each issue and mark the exact span where it occurs, using our review tooling.
- Report on overall listener satisfaction and where the narration feels natural versus where it breaks the experience.
- Work consistently and carefully across many hours of audio without losing attention to detail.
3. Who We're Looking For
- Native or near-native fluency in Italian (Italy), and an active, genuine audiobook listener, someone who listens regularly and has real opinions about what makes a good listen.
- Enough working English to report clearly and communicate with the team (you do not need to be bilingual).
- Some experience with annotation, labeling, transcription, proofreading, linguistics, or other detail-oriented review work.
- A sharp, patient ear and the discipline to stay accurate over long listening sessions.
- Reliable availability of about 20 hours per week on a part-time basis.
Nice to Have
- Prior experience on AI training-data, model-evaluation, or audio/QA annotation projects.
- A background in language, editing, voice, or audio production.
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