About the Arabic Team Lead Role

SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, is seeking an Arabic Team Lead for an hourly, remote contractor position. This role focuses on quality assurance for Arabic AI training projects and is open to candidates in Jeddah, Makkah, Saudi Arabia. The primary responsibility involves overseeing the quality, consistency, and performance of trainers and QAs across various Arabic AI content initiatives.

Role Overview and Project Context

As an Arabic Team Lead, you will review AI-generated Arabic content and the work of trainers and Quality Assurance specialists, evaluating output against project guidelines and providing precise written feedback. This position ensures that all contributors adhere to expected quality standards, assessing work for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, and meaning preservation. Your leadership will contribute directly to improving premier AI models by ensuring Arabic training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations. Please note that while there is no immediate project for this role, qualified experts will be among the first contacted for relevant future opportunities within our network.

Key Responsibilities

• Conduct spot-checks of Arabic items, identify quality issues, provide ongoing feedback, and escalate recurring or critical concerns.
• Communicate updates to trainers and QAs regarding new item guidelines, project changes, workflow adjustments, and quality expectations.
• Respond promptly to trainer and QA questions, particularly concerning Arabic wording, grammar, register, dialect, RTL formatting, translation fidelity, terminology, and cultural context.
• Manage trainer and QA activation by contacting inactive contributors, encouraging participation, tracking follow-ups, and flagging availability issues.
• Create and maintain comprehensive Arabic project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding materials.
• Schedule and conduct onboarding and training sessions for trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and Arabic-specific style requirements.
• Ensure consistent application of Arabic language guidelines by all trainers and QAs, and confirm their understanding of project updates as projects evolve.
• Identify recurring quality gaps, propose workflow improvements, and assist in building scalable QA processes for Arabic-language projects.

Qualifications and Experience

• Bachelor’s or Master’s degree in Arabic, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant related field.
• Minimum of 3 years of professional experience in Arabic writing, editing, translation, localization, content QA, AI training, education, annotation, or similar language-review workflows.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a significant advantage.

Required Skills and Attributes

• Strong understanding of Arabic grammar, spelling conventions, punctuation, diacritics, RTL formatting, tone, register, Modern Standard Arabic, and regional Arabic variations.
• Demonstrated ability to evaluate Arabic content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, incorrect register, dialect inconsistency, hallucinated claims, ambiguity, or inconsistent terminology.
• Comfortable working in fast-moving remote environments and proficient with tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the capacity to maintain style guides, FAQs, trackers, onboarding materials, honeypots, and other quality documentation.

Application Process

The selection process for this role involves an AI interview, followed by a domain-specific task, and concludes with an interview with a recruiter.