AI Language QC Specialist - Flexible Project-Based Work

Openkrill; United States; Remote; USD 42,000 - 66,000; Full time

Benefits offered by this job

Flexible project-based work

Clear guidelines and training

Job summary

Openkrill 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 transcripts are natural, readable, and faithful to project guidelines.

You will verify punctuation, capitalization, numbers, dates, times, and currencies, and ensure text normalization while maintaining original meaning.

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.
• Ability to follow detailed guidelines and apply them consistently.

Responsibilities

• Review completed Display Transcription tasks for accuracy and consistency.
• Compare the Display Transcription against the original lexical transcript and project guidelines.
• Verify punctuation, capitalization, numbers, dates, times, and currencies.
• Identify and correct formatting or guideline-related errors.
• Use reference audio to verify ambiguous cases.
• Provide accurate feedback and flag recurring issues when required.
• Complete assigned QC tasks within project deadlines and productivity targets.

Skills

Attention to detail

Excellent written English

Guideline adherence

Independent work

Transcription/proofreading experience

Job description

Openkrill 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 transcripts are natural, readable, and faithful to project guidelines.

You will verify punctuation, capitalization, numbers, dates, times, and currencies, and ensure text normalization while maintaining original meaning.