Responsable de l'assurance qualité en chinois (simplifié) (QAL)

In this hourly, remote contractor role, you will work as a Chinese (Simplified) Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Simplified Chinese AI training projects for Mainland China. You will review AI-generated Simplified Chinese 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 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 Simplified Chinese quality leadership will directly help improve the world’s premier AI models by ensuring that Chinese 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.

Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

• Spot-check Simplified Chinese items, identify issues, provide feedback through DMs, and escalate recurring or critical quality problems.
• Update trainers and QAs on Discord about new guidelines, workflow updates, project changes, and quality expectations.
• Respond to questions around Simplified Chinese wording, Mainland China terminology, punctuation, register, translation fidelity, cultural context, and edge cases.
• DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Create and maintain Simplified Chinese documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.
• Run onboarding/training calls for Simplified Chinese contributors.
• Ensure Mainland China Simplified Chinese guidelines are applied consistently.
• Identify recurring quality gaps and build scalable QA processes.

Requirements

• Bachelor’s or Master’s degree in Chinese, Mandarin, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant domain/related field.
• Native or near-native Simplified Chinese proficiency, with strong Mainland China Mandarin writing judgment.
• 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 Simplified Chinese writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
• Strong understanding of Simplified Chinese grammar, punctuation, terminology, register, tone, Mainland China Mandarin usage, cultural context, and localization expectations.
• Ability to evaluate Simplified Chinese content against detailed rubrics and identify issues such as mistranslation, Traditional Chinese leakage, unnatural tone, Taiwan/Hong Kong terminology mismatch, hallucinated claims, ambiguity, or inconsistent terminology.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Comfortable with 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 documentation.
• Experience with AI training, data annotation, LLM evaluation, prompt/response evaluation, or rubric-based LLM QA is a strong plus.