JavaScript QA Lead - Remote

Role Overview

JavaScript Quality Assurance Lead (Contract, Remote) You will oversee quality, consistency, and trainer performance across JavaScript AI training projects. This is an hourly remote contractor role supporting quality workflows for JavaScript code and AI-training outputs.

Responsibilities

• Review AI-generated JavaScript code and trainer/QA work against detailed project guidelines/rubrics.
• Evaluate quality across:
  • Correctness and runtime behavior
  • Reasoning quality and instruction-following
  • Debugging accuracy
  • Readability and maintainability
  • Performance and security awareness
  • Test coverage and formatting
  • Detection of issues such as non-executable code, flawed logic, missing edge cases, poor async handling, security risks, hallucinated APIs, and incomplete explanations.
• Spot recurring quality issues and communicate updates to trainers and QAs.
• Support onboarding and activation of contributors not working consistently.
• Maintain documentation (e.g., style guides, FAQs, trackers) and help run calibration/onboarding quality materials.

Requirements

• 3+ years professional experience in JavaScript development and/or QA/code review and related workflows (frontend, backend with Node.js, full-stack, mentoring/technical leadership).
• Strong English communication skills for following guidelines and providing clear written feedback.
• Deep knowledge of core JavaScript concepts, including: closures, scope, hoisting, prototypes, promises, async/await, event loop, modules, DOM manipulation, error handling, and modern ECMAScript.
• Ability to evaluate work using detailed rubrics and identify quality gaps.

Preferred / Nice to Have

• Familiarity with common JS tools and ecosystems: Node.js, npm/yarn/pnpm, React, Vue, Express, Jest, Mocha, Playwright, ESLint, Prettier, Vite, Webpack, Babel (and/or browser developer tools).
• Experience leading/supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, coding mentors, or QAs.
• Comfort working with remote collaboration tools such as Discord, Google Docs/Sheets, GitHub, and project trackers/dashboards.
• Experience with AI training / data annotation / LLM evaluation (prompt/response evaluation, rubric-based LLM QA).

Hiring Process & Notes

• Selection process includes: AI interview, domain-specific task, and recruiter interview.
• No immediate project is assigned; qualified candidates may be contacted first when opportunities arise via an expert network.