English Quality Assurance Lead (QAL)

In this hourly, remote contractor role, you will work as an English Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across AI training projects. You will review AI-generated English content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and help ensure that all contributors follow the expected standards. You will assess work for accuracy, clarity, reasoning quality, grammar, tone, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong 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 quality leadership will directly help improve the world’s premier AI models by ensuring that training data is accurate, consistent, 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

• Quality monitoring: Spot-check submitted 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, ensuring contributors understand instructions 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 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, and quality standards.
• Quality alignment: Ensure all trainers and QAs apply guidelines consistently and understand changes as projects evolve.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for future projects.

Requirements

• Bachelor’s or Master’s degree in English, Linguistics, Communications, Journalism, Education, AI/Data Annotation, Quality Assurance, or a relevant domain/related field.
• Strong grasp of the English language, including grammar, clarity, tone, register, structure, and written feedback quality.
• 3+ years of professional experience in quality assurance, editing, content review, AI training, education, annotation, localization QA, or related review workflows.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Strong ability to evaluate work against detailed rubrics and guidelines, identify quality gaps, and explain issues clearly and constructively.
• Excellent communication skills, including the ability to answer contributor questions, share project updates, and provide actionable feedback in writing.
• 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.
• Reliable, self-directed, and able to manage contributor activation, follow-ups, escalations, and quality checks across time zones.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus.