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SME Careers is seeking an Urdu Language Specialist (SME) for an hourly remote contractor role to review AI-generated Urdu content and create high-quality Urdu reference content, evaluating language quality and step-by-step reasoning with precise feedback.
You will assess outputs for accuracy, fluency, grammar, tone, and cultural appropriateness; write rewrites and model answers; and rate AI responses to guide model improvements.
In this hourly, remote contractor role, you will work as an Urdu Language Specialist (SME) to review AI-generated Urdu content and/or create high-quality Urdu reference content, evaluating language quality and step-by-step reasoning while providing precise written feedback.
You will assess outputs for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, and adherence to the prompt; identify errors in meaning, mistranslation, register (formal vs informal), ambiguity, RTL/script formatting, and logical consistency; fact-check claims when needed; write high-quality rewrites and model answers that demonstrate best practices in Urdu writing; and rate and compare multiple AI responses based on correctness and overall quality.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world's largest AI companies and foundation-model labs.
Your Urdu expertise directly helps improve the world's premier AI models by making their Urdu language output more natural, reliable, culturally accurate, and clearly written.
Develop AI Training Content: Create prompts and gold-standard Urdu answers across multiple domains, including general knowledge, customer support, education, conversational writing, translation, localization, and culturally sensitive content. Optimize AI Performance: Evaluate and rank AI outputs to improve fluency, correctness, tone match, register consistency, meaning preservation, RTL/script formatting, and cultural appropriateness. Ensure Model Integrity: Detect unsafe or misleading content, factual hallucinations, bias, mistranslations, ambiguity, policy-risk language, and culturally inappropriate phrasing; validate reliability across use cases.