Kannada Team Lead with Mathematical Background

In this hourly, remote contractor role, you will work as a Kannada Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Kannada AI training projects. You will review AI-generated Kannada 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 Kannada 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 Kannada quality leadership will directly help improve the world’s premier AI models by ensuring that Kannada training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations.

Responsibilities

• Quality monitoring: Spot-check Kannada items, identify quality issues, provide ongoing feedback through DMs, and escalate 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 Kannada wording, register, translation fidelity, cultural context, regional variation, 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 Kannada 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 Kannada-specific style requirements.
• Quality alignment: Ensure all trainers and QAs apply Kannada 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 Kannada-language projects.

Requirements

• Bachelor’s or Master’s degree in Mathematics
• Native or near-native Kannada 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 Kannada writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
• Strong understanding of Kannada grammar, spelling conventions, punctuation, tone, register, regional variation, and cultural context.
• Ability to evaluate Kannada content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, 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.