Expert Audio Transcriber, Azerbaijani

Job description

THE ROLE We are hiring expert Azerbaijani transcribers to convert audio recordings of native speakers into precise, conventionally formatted written transcripts. The work powers speech recognition, dialogue, and evaluation systems for major AI labs. Audio ranges from clean studio recordings to natural conversational speech in real-world acoustic conditions. You will be working at the level of expert linguistic judgment. This is not data entry. Decisions about orthography, code-switching, speaker attribution, disfluencies, and non-speech events shape the quality of every model trained on your output. DIALECT AND SCOPE Azerbaijani (Azerbaijani Turkish) in its Northern variety (Azerbaijan, Latin script) and Southern variety (Iranian Azerbaijan, Perso-Arabic script). The project lead will define the target variety. Primary speaker regions: Baku, Ganja, Sumgait, Mingachevir, Tabriz, Urmia. WHAT YOU WILL DO Transcribe audio recordings into accurate, time-aligned written text following Perle's project-specific style guide Apply correct conventions for speaker identification, overlapping speech, disfluencies, fillers, false starts, and non-speech events such as laughter, background noise, and music Make principled orthographic decisions for dialectal, colloquial, and code-switched speech Flag ambiguous segments, uncertain speakers, and audio quality issues using project conventions Self-review every submission against Perle's quality bar before delivery Engage with QA reviewers on edge cases, calibration sessions, and style guide updates MUST HAVE Native fluency in Azerbaijani with lifelong residence or deep immersion in your declared variety's region Mastery of the orthography appropriate to your variety (Latin for Northern, Perso-Arabic for Southern) Ability to distinguish North and South Azerbaijani in mixed audio Comfortable transcribing code-switching with Russian (Northern) or Persian (Southern) At least two years of professional transcription, captioning, court reporting, broadcast, translation, or linguistic annotation experience Demonstrated accuracy at the word level under deadline pressure Comfortable with web-based annotation platforms and variable-speed audio playback Reliable high-speed internet, quality headphones, and a quiet workspace Ability to commit to defined volume per week during active project phases NICE TO HAVE Background in Turkic linguistics, translation, journalism, or media Prior experience annotating Azerbaijani or Turkic speech data Working knowledge of Turkish or Persian for cross-lingual audio Familiarity with IPA or other phonetic transcription systems Experience with verbatim versus clean-verbatim transcription conventions HOW WE WORK WITH YOU Paid pilot task before full onboarding so both sides can calibrate Per-task or per-minute compensation, with rates tied to audio complexity and turnaround Flexible scheduling, with volume scaling up and down based on active client projects Direct working relationship with Perle's linguistic leads and QA team Eligible for invitation to additional Perle expert projects across related domains HOW TO APPLY Apply through Winnow, Perle's job portal at . Find the role for your language under Open Jobs, set up your candidate profile, and complete the fully automated interview, which takes about 25 minutes. We review applications within 24 hours and invite passing candidates straight to the project. Apply here: EQUAL OPPORTUNITY Perle hires experts on the basis of skill, language proficiency, and demonstrated quality. We welcome applicants from every background and every region where our target languages are spoken. ABOUT PERLE Perle is an AI infrastructure company building expert-driven training data, evaluation systems, and applied AI products for the world's leading labs and enterprises. Our partners include xAI, Samsung, ELM, and Unisys. Headquartered in San Francisco with experts across more than forty markets, we specialize in the work that requires real human judgment: domain expertise, linguistic nuance, and cultural fidelity that generic data vendors cannot deliver. Apply here: