Architecture & Design QA Lead - Remote

About the role

This role is a fast-growing AI Data Services company 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.

Responsibilities

• 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.

Requirements

• Bachelor’s, Master’s, or professional degree in Architecture, Interior Design, Urban Design, Landscape Architecture, Environmental Design, Industrial Design, Planning, or a closely related field.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
• 3+ years of experience in architecture, design, interiors, urban design, design research, construction documentation, teaching, design review, visualization, or related built-environment workflows.
• Strong understanding of design principles, spatial organization, circulation, scale, proportion, materiality, accessibility, sustainability, building systems, construction logic, and design communication.
• Ability to evaluate architecture/design content against detailed rubrics and identify issues such as impractical layouts, poor spatial reasoning, code-insensitive suggestions, weak design rationale, accessibility gaps, unsafe assumptions, or unrealistic construction recommendations.
• Familiarity with tools or methods such as AutoCAD, Revit, Rhino, SketchUp, Adobe Creative Suite, BIM, construction drawings, precedent analysis, site analysis, rendering, or design presentations is preferred.
• Experience leading or supporting remote teams of designers, reviewers, annotators, educators, visual QA specialists, or domain experts 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, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, design QA, visual review, architectural review, or rubric-based review is a strong plus.