Remote Arabic AI Linguist: Data Annotation & Evaluation

Mercor; United States; Remote; USD 57,000 - 81,000; Full time

Job summary

Mercor is seeking experienced Arabic Linguistic Experts to evaluate LLM performance on Arabic tasks and curate high-quality Arabic linguistic data for model training. The role involves editing, annotation, and providing linguistic feedback to guide ongoing AI research efforts.

Qualified candidates are native or near-native in Arabic with translation/editing experience, strong linguistic insight, and the ability to work effectively in distributed, remote teams.

Qualifications

• Native or near-native fluency in Arabic with strong grammar and style sense.
• Professional experience in translation, editing, linguistic annotation, or NLP data work in Arabic.
• Understanding of linguistic nuances and regional variations.
• Interest in AI and LLM-related language technologies is a plus.
• Excellent attention to detail and ability to spot subtle phrasing issues.
• Clear communication and experience working in distributed, remote teams.

Responsibilities

• Evaluate quality and accuracy of LLM-generated Arabic text across use cases.
• Edit and refine Arabic content to meet linguistic and cultural standards.
• Annotate Arabic text for grammatical, syntactic, and semantic features.
• Identify model errors and gaps; collaborate with researchers to improve performance.
• Provide detailed linguistic feedback to guide model training.

Skills

Arabic fluency

Editing

Linguistic annotation

Translation experience

Attention to detail

Remote collaboration

Job description

Mercor is seeking experienced Arabic Linguistic Experts to evaluate LLM performance on Arabic tasks and curate high-quality Arabic linguistic data for model training. The role involves editing, annotation, and providing linguistic feedback to guide ongoing AI research efforts.

Qualified candidates are native or near-native in Arabic with translation/editing experience, strong linguistic insight, and the ability to work effectively in distributed, remote teams.