Expert Audio Transcriber, Croatian
Perle SystemsABOUT 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: https://winnow.perle.ai/jobs/76592750-ce6a-4a73-94c5-8a887781c5d6
THE ROLE
We are hiring expert Croatian 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
Standard Croatian and its three major dialect groups (Stokavian, Kajkavian, Chakavian). Project audio reflects regional speech with awareness of distinctions from Serbian, Bosnian, and Montenegrin.
Primary speaker regions: Zagreb, Split, Rijeka, Osijek, Zadar, Dubrovnik.
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 n
Opens the company's application page
Listed via
Himalayas
himalayas.app
Similar roles
Design & Tech
Related reads from TCHNX
AI Training Data Is Poisoning Design Trends. Here's How to Spot It
As generative AI tools flood the design industry, their training datasets are creating a homogenised aesthetic. We investigate how outdated samples are flattening creativity and what designers can do about it.
Why Do AI-Generated Algorithmic Interfaces Feel Wrong?
AI-optimized interfaces may be mathematically efficient, but they often violate the psychological principles humans expect. I examine why algorithmic design creates friction, even when the data suggests it shouldn't.
Why AI Design Tools Are Quietly Replacing Junior Designers and What Actually Comes Next
AI tools promise efficiency, but London studios are discovering an unexpected paradox: automation creates new bottlenecks requiring precisely the expertise being eliminated. We investigate what's actually happening to entry-level design work.