HTML/CSS Quality Assurance Lead (QAL)

Job Summary

As an HTML/CSS Quality Assurance Lead (QAL), you will oversee quality, consistency, and contributor performance across HTML/CSS AI training projects.

This role involves reviewing AI-generated frontend code, evaluating trainer and QA outputs, maintaining quality standards, and ensuring training data aligns with project requirements and client expectations.

Your work will directly contribute to improving advanced AI systems by ensuring frontend training content is accurate, accessible, responsive, maintainable, and clearly explained.

Key Responsibilities

Quality Monitoring

• Spot-check HTML/CSS training tasks and QA outputs
• Identify recurring quality issues and frontend implementation gaps
• Provide actionable written feedback and escalate critical concerns

Frontend Code Review

• Review and evaluate:

  • HTML implementations
  • CSS layouts
  • Responsive designs
  • Accessibility improvements
  • UI components
  • Debugging responses
  • Frontend explanations

• Assess work for:

  • Semantic correctness
  • Accessibility compliance
  • Responsive behavior
  • Browser compatibility
  • CSS architecture
  • Maintainability
  • Performance
  • Instruction adherence

Trainer & QA Communication

• Communicate updates regarding:
  • Guidelines
  • Workflow changes
  • HTML/CSS review standards
  • Accessibility requirements
  • Quality expectations

Contributor Support

• Answer questions involving:
  • Semantic HTML
  • Forms and tables
  • ARIA basics
  • Accessibility
  • CSS specificity
  • Flexbox
  • CSS Grid
  • Responsive design
  • Browser compatibility
  • Rubric interpretation

Activation Management

• Follow up with inactive contributors
• Track engagement and participation
• Report contributor availability concerns

Documentation & Onboarding

• Create and maintain:

  • Style guides
  • Documentation
  • FAQs
  • Examples
  • Calibration tasks
  • Onboarding materials

• Conduct onboarding and training sessions for contributors

Accessibility & Quality Review

• Identify and flag:
  • Invalid HTML markup
  • Accessibility violations
  • Poor responsive behavior
  • Layout issues
  • CSS specificity problems
  • Browser compatibility issues
  • Brittle implementations
  • Non-production-ready recommendations

Process Improvement

• Improve QA workflows and review processes
• Identify recurring quality gaps and implement corrective actions

Required Qualifications

• Bachelor's or Master's degree in:
  • Computer Science
  • Web Development
  • UX/UI Design
  • Information Technology

or equivalent professional frontend development experience.

• Strong English communication skills

• Minimum 3 years of experience in:

  • HTML/CSS development
  • Frontend engineering
  • Web accessibility
  • Responsive design
  • UI implementation
  • Code review
  • Quality assurance
  • Technical mentoring

• Strong understanding of:

  • Semantic HTML
  • Forms
  • Tables
  • ARIA fundamentals
  • Accessibility principles
  • CSS selectors
  • Cascade and specificity
  • Box model
  • Flexbox
  • CSS Grid
  • Positioning
  • Responsive layouts
  • Media queries
  • CSS animations
  • Modern CSS features

• Ability to identify:

  • Invalid markup
  • Accessibility issues
  • Layout bugs
  • Responsiveness problems
  • CSS architecture issues
  • Browser compatibility concerns
  • Incomplete explanations

Preferred Qualifications

• Experience with:

  • Sass
  • Tailwind CSS
  • Bootstrap
  • CSS Modules
  • Design Systems

• Familiarity with:

  • Browser DevTools
  • Lighthouse
  • WCAG guidelines
  • Figma handoff workflows
  • GitHub
  • CI/CD pipelines
  • Frontend build tools

• Experience leading:

  • Frontend developers
  • QA teams
  • Trainers
  • Reviewers
  • Remote contributors

• Experience with:

  • AI training
  • Data annotation
  • LLM evaluation
  • Accessibility review
  • Frontend code QA
  • Rubric-based technical review

Why Join

• Help improve leading AI systems through frontend quality assurance
• Flexible remote schedule
• Weekly payments
• Referral rewards and community incentives
• Access to future opportunities through SME Careers' expert network

Selection Process

1. AI Interview
2. Domain-Specific Assessment
3. Recruiter Interview

Important Note

There is currently no active project for this role. Qualified candidates will be added to the expert network and contacted when relevant opportunities become available.