Transcription Expert (Telugu)
The work
You will prepare Telugu language data for AI training by turning audio, video, and text into accurate written content. You will also annotate and segment audio, review existing transcriptions, and explain language or cultural details that affect the data.
- Transcribe Telugu audio, video, and text with close attention to linguistic detail.
- Annotate, segment, and label audio using project guidelines.
- Review and edit Telugu transcriptions for accuracy, consistency, clarity, and correctness.
- Identify language nuances, regional differences, and unclear wording.
- Provide cultural and contextual insights that improve the authenticity of Telugu language data.
- Manage tasks independently and deliver accurate work against project milestones.
What it pays and takes
Prior AI experience is not required, but practical experience with Telugu language data, transcription, or annotation can help.
- Pay: $10 to $24 per hour equivalent, paid per task.
- Time: At least 20 hours per week.
- Location: Remote and open to candidates in the eligible countries for this role across the Americas, Europe, Asia, Africa, and the Middle East.
- Language: Professional fluency in Telugu, with excellent written and spoken Telugu.
- Experience: Demonstrated experience in transcription, annotation, or linguistic data processing involving audio, video, or text.
- Skills: Strong proofreading, editing, communication, attention to detail, and independent task management.
- Helpful background: Familiarity with linguistic annotation tools or transcription platforms.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 5 screening questions.
About AI training work
AI training is the human work behind systems that learn from examples, including transcribed and annotated language data. People with strong language skills and careful judgment help make those examples accurate and useful, especially when regional and cultural meaning matters.
