Remote AI Narration Quality Auditor (Part-Time)

Obsidian; San Francisco (CA); Remote; USD 21,000 - 30,000; Part time

Job summary

Obsidian is seeking native English speakers who love audiobooks to evaluate AI-narrated titles. Work remotely on a part-time basis, about 20 hours per week, with flexible daily schedule. Your role is to listen like a fan but with an analytical ear, flagging moments where narration slips so models can improve.

You should have strong English, experience with annotation or linguistic review, and a patient, detail-focused approach. Prior work in QA for audio or training-data is a plus.

Qualifications

• Native or near-native English (US) with a genuine audiobook listening habit.
• Able to report clearly and communicate with the team.
• Some experience with annotation, labeling, transcription, proofreading, linguistics, or other detail-oriented review work.
• Patient, sharp ear and disciplined for long listening sessions.
• Reliable availability of about 20 hours per week.

Responsibilities

• Listen to AI-narrated audiobooks in English 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.

Job description

Obsidian is seeking native English speakers who love audiobooks to evaluate AI-narrated titles. Work remotely on a part-time basis, about 20 hours per week, with flexible daily schedule. Your role is to listen like a fan but with an analytical ear, flagging moments where narration slips so models can improve.

You should have strong English, experience with annotation or linguistic review, and a patient, detail-focused approach. Prior work in QA for audio or training-data is a plus.