Remote Brazilian Portuguese Audio Transcript Editor

Alignerr Corp.; Lisboa; Remote; EUR 21,000 - 34,000; Full time

Job summary

Alignerr is seeking an Audio Transcript Editor fluent in Brazilian Portuguese to review and clean machine-generated transcripts for AI training. This remote contract role offers flexible hours and a focus on precision, timing, and formatting across Brazilian Portuguese transcripts.

Ideal candidates will have native-level language skills, sharp listening abilities, and a meticulous approach to ensure word-for-word accuracy and consistent style across large transcript datasets.

Qualifications

• Native or near-native Brazilian Portuguese speaker with excellent written Portuguese.
• Strong listening skills and ability to parse spoken language accurately.
• Exceptional attention to detail and methodical approach.
• Ability to follow detailed style guides and formatting rules precisely.

Responsibilities

• Listen to audio recordings and compare them against machine-generated transcripts in Brazilian Portuguese.
• Identify and correct misspellings, missing words, incorrect words, and punctuation issues.
• Ensure word-level accuracy and verify timestamps align with audio.
• Apply detailed style and formatting guidelines consistently across tasks.
• Flag sections with unclear audio or quality issues for review.
• Maintain high accuracy on large volumes of transcript data.
• Work independently and asynchronously on your own schedule.

Skills

Brazilian Portuguese

Attention to detail

Listening skills

Independent work

Tools

Audio playback tools

Job description

Alignerr is seeking an Audio Transcript Editor fluent in Brazilian Portuguese to review and clean machine-generated transcripts for AI training. This remote contract role offers flexible hours and a focus on precision, timing, and formatting across Brazilian Portuguese transcripts.

Ideal candidates will have native-level language skills, sharp listening abilities, and a meticulous approach to ensure word-for-word accuracy and consistent style across large transcript datasets.