JavaScript Team Lead

Pay: up to $70/hour

In this hourly, remote contractor role, you will work as a JavaScript Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across JavaScript AI training projects. You will review AI-generated JavaScript 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, 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 JavaScript 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 Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your JavaScript quality leadership will directly help improve the world’s premier AI models by ensuring that JavaScript training data is accurate, 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.

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