AI Language QC Specialist - Flexible, Impactful

Lever, Inc.; Kuala Lumpur; On-site; MYR 33,000 - 67,000; Full time

Job summary

Lever, Inc. is seeking quality control specialists to support an AI language project focused on improving speech recognition systems. As a QC contributor, you will review completed language annotation tasks and ensure AI-generated transcripts are natural, readable and aligned with project guidelines.

You will focus on punctuation, capitalization, numbers, dates, currencies and text normalization, using reference audio to resolve ambiguities and ensure the original meaning is preserved.

Qualifications

• Excellent written skills in the target language.
• Strong attention to detail and accuracy.
• Good understanding of punctuation, capitalization, and written language conventions.
• Ability to identify inconsistencies and errors in formatted text.

Responsibilities

• Review completed Display Transcription tasks for accuracy and consistency.
• Compare the Display Transcription against the original lexical transcript and project guidelines.
• Verify the correct use of punctuation, capitalization, numbers, dates, times, and currencies.
• Ensure that the original words and meaning have been preserved.
• Identify and correct formatting or guideline-related errors.
• Use the reference audio when necessary to verify ambiguous cases.
• Apply project-specific quality standards consistently.
• Provide accurate feedback and flag recurring issues when required.
• Complete assigned QC tasks within project deadlines and productivity targets.

Skills

Attention to detail

Language proficiency

Transcription proofreading

Job description

Lever, Inc. is seeking quality control specialists to support an AI language project focused on improving speech recognition systems. As a QC contributor, you will review completed language annotation tasks and ensure AI-generated transcripts are natural, readable and aligned with project guidelines.

You will focus on punctuation, capitalization, numbers, dates, currencies and text normalization, using reference audio to resolve ambiguities and ensure the original meaning is preserved.