Language Expert (Korean Audio Transcription)

You will prepare Korean and English audio data for AI training. The work focuses on accurate transcription, quality review, and audio annotation.

You will also coordinate with project staff, complete assignments within project timelines, and share feedback about workflows and tools.

• Transcribe Korean and English audio samples accurately.
• Review, edit, and proofread transcriptions for quality.
• Add relevant metadata and contextual notes to audio files.
• Follow project specifications and manage multiple assignments.
• Provide feedback on workflows and tool usability.

What it pays and takes

This is a part-time contractor role for a Korean language expert. No previous AI training experience is required, but professional transcription experience and careful written work are important.

• Pay: $20 to $36 per hour.
• Time: Less than 20 hours per week.
• Work type: Part-time contract work.
• Location: Open to candidates in the countries listed for this role.
• Languages: Professional fluency in Korean, plus strong spoken and written comprehension in English.
• Requirements: Professional audio transcription experience, strong attention to detail, familiarity with standard transcription tools, and clear written and verbal communication.
• You should be able to manage multiple assignments and adapt to changing requirements.
• Helpful background: AI training, data annotation, or language resource development experience.

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 4 screening questions.

About AI training work

AI training is the human work behind systems that understand language, audio, and other data. Contributors transcribe, label, review, and improve examples so AI models can learn from them.