Audiobook QA Expert — Spanish (US)

Help make the next generation of AI-narrated audiobooks sound genuinely human.

1. Overview

We're building a team of native Spanish speakers who love audiobooks to help evaluate AI-narrated (text-to-speech) audiobooks in US / North American Spanish. 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 Spanish is natively spoken.

2. What You'll Do

• Listen to AI-narrated audiobooks in Spanish 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 Spanish (US / North American Spanish), 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.