Remote Japanese Audio Transcript Editor for AI Training

Alignerr Corp.; Greater London; Remote; GBP 14,000 - 55,000; Part time

Job summary

Alignerr is seeking a meticulous Japanese Audio Transcript Editor to clean and polish transcripts for AI training. You will ensure word-level accuracy, correct kanji and punctuation, and align transcripts precisely with audio.

This remote, hourly contract role suits detail-oriented native Japanese speakers who can work independently on flexible hours. Responsibilities include reviewing transcripts, correcting errors, and applying strict style guidelines across high-volume datasets.

Qualifications

• Native level Japanese; excellent listening and reading/writing in Japanese.
• Meticulous attention to detail; you notice subtle errors others miss.
• Able to follow detailed style guides and formatting rules precisely.
• Comfortable with word-level timing and transcript accuracy.
• Work independently and asynchronously; remote/flexible schedule.

Responsibilities

• Listen to Japanese audio, review transcripts for accuracy.
• Correct transcription errors: misheard words, missing segments, kanji/punctuation.
• Verify and adjust word-level timing to match audio; apply style guidelines.
• Identify unclear or low-quality audio per project protocols.
• Maintain high accuracy and consistency across large transcript datasets.
• Work on a flexible schedule, asynchronously.

Skills

Native Japanese

Listening comprehension

Attention to detail

Japanese reading/writing

Follow style guides

Independent work

Word-level timing

Remote-friendly

Job description

Alignerr is seeking a meticulous Japanese Audio Transcript Editor to clean and polish transcripts for AI training. You will ensure word-level accuracy, correct kanji and punctuation, and align transcripts precisely with audio.

This remote, hourly contract role suits detail-oriented native Japanese speakers who can work independently on flexible hours. Responsibilities include reviewing transcripts, correcting errors, and applying strict style guidelines across high-volume datasets.