Arabic/English Bilingual AI Data Annotator & Evaluator (Remote, Hourly Contractor)

Position Summary This is a fully remote, hourly contractor role supporting AI data and language projects on a project-based, flexible hour basis. Project scopes vary and may include: • Data annotation: labeling, classifying, and structuring documents, tables, and other content to support AI training datasets. • LLM evaluation: reviewing AI-generated responses for accuracy, reasoning quality, coherence, and cultural/linguistic appropriateness. • Content generation: writing high-quality prompts and model responses to guide AI learning across diverse topics. • Localization QA: ensuring terminology, tone, cultural nuance, and locale-specific details (units, references, names, dates) are consistently accurate across outputs. You will apply senior-level Arabic localization and translation judgment alongside minimum C1 English proficiency to ensure terminology, tone, and cultural nuance are consistently accurate across use cases. Profile Requirements • Bachelor's degree or higher in Translation, Linguistics, Localization, Communications, or a related field. • Native or near-native Arabic proficiency with strong writing and editing skills across Modern Standard Arabic and at least one dialect. • Minimum C1 English proficiency (reading/writing) for interpreting prompts, source materials, and evaluation guidelines. • Excellent editorial judgment in register, tone, punctuation, inclusivity, and cultural nuance, with extreme attention to detail. • Ability to identify meaning drift, ambiguity, locale inconsistencies, and subtle errors, and to explain corrections clearly in writing. • Ability to rigorously fact-check localized content (units, references, names, dates) using reliable sources and consistent reasoning. • Ability to identify reasoning gaps, methodological errors, and unclear explanations even when language is fluent. • Reliable, self-directed, and able to deliver consistent quality with clear communication and responsiveness across time zones. Preferred Experience • Professional localization or translation experience. • Familiarity with MQM/LQA concepts (severity, category, and root-cause thinking) for consistent quality decisions. • Proficiency with CAT tools and QA workflows (translation memory, termbases, automated checks). • Previous experience with AI data training, annotation, or evaluation. About CNTXT AI CNTXT AI builds artificial intelligence products and data solutions with a focus on making AI accurate, safe, and globally relevant for impact. Our work spans data services, custom AI solutions, and proprietary AI products, with deep expertise in Arabic-native and secure, sovereign solutions.

Bachelor's degree or higher in Translation, Linguistics, Localization, Communications, or a related field. Native or near-native Arabic proficiency with strong writing and editing skills across Modern Standard Arabic and at least one dialect. Minimum C1 English proficiency (reading/writing) for interpreting prompts, source materials, and evaluation guidelines. Excellent editorial judgment in register, tone, punctuation, inclusivity, and cultural nuance, with extreme attention to detail. Ability to identify meaning drift, ambiguity, locale inconsistencies, and subtle errors, and to explain corrections clearly in writing. Ability to rigorously fact-check localized content (units, references, names, dates) using reliable sources and consistent reasoning. Ability to identify reasoning gaps, methodological errors, and unclear explanations even when language is fluent. Reliable, self-directed, and able to deliver consistent quality with clear communication and responsiveness across time zones.

Data annotation: labeling, classifying, and structuring documents, tables, and other content to support AI training datasets. LLM evaluation: reviewing AI-generated responses for accuracy, reasoning quality, coherence, and cultural/linguistic appropriateness. Content generation: writing high-quality prompts and model responses to guide AI learning across diverse topics. Localization QA: ensuring terminology, tone, cultural nuance, and locale-specific details (units, references, names, dates) are consistently accurate across outputs.