Remote Arabic AI Localization Annotator & Evaluator

CNTXT AI; United States; Remote; USD 30,000 - 83,000; Part time

Job summary

CNTXT AI is hiring for a fully remote, hourly contractor role to support AI data and language projects. You will apply senior-level Arabic localization and translation judgment across diverse tasks, ensuring terminology, tone, and cultural nuance align with project guidelines.

Ideal candidates hold a Bachelor's in Translation/Linguistics, native Arabic proficiency, and C1 English, with strong editorial judgment and meticulous attention to detail for accurate localization across contexts.

Qualifications

• Bachelor's degree or higher in Translation, Linguistics, Localization, or related field.
• Native or near-native Arabic with strong writing/editing in Modern Standard Arabic and at least one dialect.
• Minimum C1 English proficiency for interpreting prompts and evaluation guidelines.
• Excellent editorial judgment in register, tone, punctuation, inclusivity, and cultural nuance.

Responsibilities

• Data annotation: label, classify, and structure documents and content for AI training datasets.
• LLM evaluation: assess AI responses for accuracy, reasoning, coherence, and cultural appropriateness.
• Content generation: write high-quality prompts and model responses to guide AI learning.
• Localization QA: ensure terminology, tone, and locale nuances are accurate across outputs.

Skills

Arabic localization

English proficiency

Editorial judgment

Attention to detail

Education

Bachelor's degree in Translation/Linguistics

Linguistics degree

Tools

CAT tools

Job description

CNTXT AI is hiring for a fully remote, hourly contractor role to support AI data and language projects. You will apply senior-level Arabic localization and translation judgment across diverse tasks, ensuring terminology, tone, and cultural nuance align with project guidelines.

Ideal candidates hold a Bachelor's in Translation/Linguistics, native Arabic proficiency, and C1 English, with strong editorial judgment and meticulous attention to detail for accurate localization across contexts.