TypeScript Team Lead

In this hourly, remote contractor role, you will work as a TypeScript Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across TypeScript AI training projects. You will review AI-generated TypeScript code 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 code correctness, type safety, reasoning quality, runtime behavior, debugging accuracy, readability, maintainability, performance, security awareness, test coverage, 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 TypeScript expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your TypeScript quality leadership will directly help improve the world’s premier AI models by ensuring that TypeScript training data is accurate, type-safe, executable, logically sound, clearly explained, well-documented, 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.

Requirements: - 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.