Architecture and Design Quality Assurance Lead; QAL

Pay: up to $75/hour

In this hourly, remote contractor role, you will work as an Architecture / Design Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across architecture, interior design, urban design, and built-environment AI training projects. You will review AI-generated architecture/design 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 design accuracy, spatial reasoning, architectural terminology, building-systems awareness, code/safety sensitivity, sustainability considerations, visual/design logic, clarity, formatting, instruction-following, 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 architecture/design expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your architecture/design quality leadership will directly help improve the world’s premier AI models by ensuring that architecture and design training data is accurate, context-aware, visually coherent, practical, well-explained, 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.

• Quality monitoring:
  Spot-check architecture/design items, identify quality issues, provide ongoing feedback through DMs, and
  ** escalate
  * * recurring or critical issues.
• Design review:
  Evaluate AI-generated architecture/design explanations, spatial layouts, design concepts, material recommendations, building-system descriptions, accessibility considerations, and design reasoning for accuracy and practicality.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and architecture/design-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around spatial logic, design terminology, accessibility, sustainability, construction feasibility, materials, and rubric interpretation.
• 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 architecture/design project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, 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 architecture/design-specific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply architecture/design review guidelines consistently and understand updates as projects evolve.
• Risk review:
  Flag unsafe, inaccessible, impractical, misleading, code-insensitive, or poorly contextualized architecture/design recommendations.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for architecture/design AI training projects.