Team Lead, AI Training QA

As an Arabic Team Lead, you will review AI-generated Arabic content and trainer quality checks against project rubrics and style guidance. You will help keep Arabic AI training work accurate, clear, consistent, and appropriate for its intended audience.

The duties and qualifications for this role should be reviewed before applying.

• Review Arabic AI training outputs and trainer quality checks.
• Check grammar, spelling, punctuation, tone, register, dialect consistency, translation accuracy, terminology, and right-to-left formatting.
• Identify mistranslations, literal wording, unclear meaning, incorrect register, unsupported claims, and inconsistent terminology.
• Give clear written feedback and communicate updates to trainers and quality reviewers.
• Track recurring quality issues and help resolve ongoing quality gaps.
• Maintain style guides, trackers, FAQs, examples, quality checks, and onboarding materials.
• Support onboarding and training for contributors working on Arabic-language quality workflows.
• Help improve repeatable quality processes for Arabic AI training projects.

• Pay: $35 to $40 per hour.
• Time: 20+ hours per week.
• Work arrangement: Part-time contract role.
• Location: Open to candidates in Egypt and Saudi Arabia.
• Languages: Fluent Arabic with strong reading and writing skills; strong English communication skills.
• Experience: Arabic writing, editing, translation, localization, content quality assurance, AI training, annotation, or related language-review work.
• Knowledge: Arabic grammar, spelling conventions, punctuation, diacritics, right-to-left formatting, tone, register, Modern Standard Arabic, and regional variation.
• Skills: Evaluate content against detailed rubrics and identify meaning, tone, register, terminology, and formatting problems.
• Preferred background: Supporting or leading remote teams of trainers, reviewers, editors, or quality reviewers.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind modern AI systems. People review and improve examples, text, and model responses so systems can produce more accurate and useful results. Experienced contributors are often paid for their subject or language knowledge because careful review affects how the AI behaves.