QA Linguist – Arabic (Morocco) & Arabizi | AI Data Annotation

OVERVIEW We’re looking for meticulous and skilled QA Linguists to join our team for an exciting data annotation project. You’ll play a key role in performing linguistic QA for emerging AI technologies, ensuring data accuracy and quality. This is a temporary, part-time freelance opportunity with flexible hours (20–40 hours per week), and the potential for contract extension based on performance and project needs. MAIN DUTIES - Manage and mentor a team of QA Analysts dedicated to data annotation - Perform QA in parallel with production to promptly identify and address poor performance or substandard work. - Perform data annotation and quality assurance tasks - Collaborate effectively with team members on-site to meet project goals - Ensure secure handling of data and uphold strict confidentiality standards Project Details Job Title: QA Linguist - Data Annotation Specialist Schedule: from 20 hours to 40 hours per week This is a freelance opportunity; the workload is based on project needs. Weekly hours may vary. Employment Type: Freelance/Independent Contract Location: Remote Language: Arabic (Morocco), Arabizi, and English Rate: $21 USD per hour Qualifications

Full proficiency in Moroccan Arabic

Familiarity with Arabizi (informal and conversational language used in casual contexts like social media & texting)

Advanced competency in English to follow instructions and communicate with our team

3+ years of experience as a linguist or in a related field, such as language research, translation, localization, or content creation, with a strong focus on language accuracy.

Strong background in quality assurance, data annotation, team management, and data handling.

Ability to dedicate a minimum of 15-20 hours per week to the project

Strong attention to detail and accuracy

Capability to perform sufficient research within the allocated time, working within short throughputs

Reliable computer system and internet connection

Must sign a Non-Disclosure Agreement to protect client confidentiality