TypeScript QA Lead - Remote

About This Role

Your Profile

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or a closely related field; equivalent professional software engineering experience may be considered.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback in English.
• 3+ years of professional experience in TypeScript development, JavaScript development, frontend engineering, backend development with Node.js, full-stack engineering, code review, software QA, technical mentoring, or related workflows.
• Strong understanding of core TypeScript concepts such as static typing, interfaces, type aliases, generics, unions/intersections, narrowing, inference, mapped types, conditional types, utility types, strict mode, module systems, and type-safe API design.
• Strong understanding of JavaScript runtime behavior, including promises, async/await, event loop, closures, scope, modules, error handling, and modern ECMAScript features.
• Ability to evaluate TypeScript content against detailed rubrics and identify issues such as incorrect logic, non-executable code, type errors, unsafe any usage, flawed reasoning, missing edge cases, poor async handling, security risks, hallucinated APIs, or incomplete explanations.
• Familiarity with common TypeScript ecosystems and tools such as Node.js, React, Next.js, Express/NestJS, npm/yarn/pnpm, Jest, Vitest, Playwright, ESLint, Prettier, Vite, Webpack, tsconfig, or GitHub workflows is preferred.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, coding mentors, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, code content QA, or rubric-based LLM evaluation is a strong plus.

Key responsibilities

• Quality monitoring: Spot-check TypeScript items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Code review: Evaluate AI-generated TypeScript code, type definitions, debugging responses, implementation explanations, frontend/backend snippets, tests, and step-by-step reasoning for correctness, type safety, and clarity.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and TypeScript-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around type safety, inference, generics, strict mode, async logic, browser vs Node.js environments, frameworks, testing, edge cases, 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 TypeScript 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 TypeScript-specific review requirements.
• Quality alignment: Ensure all trainers and QAs apply TypeScript review guidelines consistently and understand updates as projects evolve.
• Risk and security review: Flag unsafe, misleading, insecure, or overconfident code recommendations, especially around type bypassing, unsafe casts, dependency usage, authentication, data handling, and production readiness.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for TypeScript AI training projects.