Urdu Team Lead

SME Careers; Pakistan; Remote; PKR 4,581,000 - 9,543,000; Part time

Job summary

SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, seeks an Urdu Quality Assurance Lead (QAL) for an hourly remote contractor role to oversee quality across Urdu AI training projects. You will review AI-generated Urdu content, ensure adherence to guidelines, provide precise feedback, and coordinate with distributed trainers and QAs via Discord.

Strong Urdu and English skills, attention to detail, and experience in remote QA workflows are essential.

Qualifications

• Bachelor' s or master's degree in Urdu or related field.
• Native or near-native Urdu with strong reading and writing skills.
• Excellent English reading and writing to follow guidelines and provide feedback.

Responsibilities

• Quality monitoring: spot-check Urdu items and provide ongoing feedback.
• Communicate updates to trainers and QAs on Discord about guidelines and changes.
• Respond to trainer/QA questions clearly around Urdu wording, culture, and formatting.
• Activate inactive contributors and manage follow-ups.
• Create and maintain Urdu project documentation and onboarding materials.
• Schedule and run onboarding/training calls explaining expectations and rubrics.
• Ensure Urdu guidelines are followed and updated as projects evolve.

Skills

Urdu writing

Editing

Localization

Quality assurance

Remote team management

Structured feedback

Attention to detail

Communication in English

Discord usage

Education

Urdu/Linguistics/Translation degree

Tools

Discord

Google Sheets

Google Docs

Dashboards

Project management tools

Job description

In this hourly, remote contractor role, you will work as an Urdu Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Urdu AI training projects. You will review AI-generated Urdu content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, meaning preservation, instruction-following, formatting, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong Urdu and English skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote teams.

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 quality leadership will directly help improve the world's premier AI models by ensuring that Urdu training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Key Responsibilities

• Quality monitoring: Spot-check Urdu items, identify quality issues, provide ongoing feedback through DMs, and upscale recurring or critical issues.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, and quality expectations.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around Urdu wording, register, translation fidelity, cultural context, RTL formatting, Nastaliq script conventions, Pakistan vs India usage, and edge cases.
• Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
• Documentation: Create and maintain Urdu project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding materials.
• Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and Urdu-specific style/script requirements.
• Quality alignment: Ensure all trainers and QAs apply Urdu language guidelines consistently and understand updates as projects evolve.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for Urdu-language projects.

Your Profile

• Bachelor's or Master's degree in Urdu, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant domain/related field.
• Native or near-native Urdu proficiency with strong reading and writing skills.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear feedback in English.
• 3+ years of professional experience in Urdu writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
• Strong understanding of Urdu grammar, spelling conventions, punctuation, tone, register, cultural context, Nastaliq script conventions, and right-to-left formatting.
• Comfortable handling Pakistan Urdu, India Urdu, formal/literary Urdu, and conversational usage, depending on project requirements.
• Ability to evaluate Urdu content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, incorrect register, script/formatting problems, hallucinated claims, ambiguity, or inconsistent terminology.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, and other quality documentation.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus.