Junior JavaScript Developer

About the Role We are an AI data annotation and labeling contracting company training the next generation of Large Language Models (LLMs) and code-generation AI tools. We are seeking a detail-oriented Junior JavaScript Developer to review, evaluate, author, and annotate JavaScript code samples used in AI training datasets. In this role, you will help teach AI models how to write clean, secure, and efficient JavaScript code through Reinforcement Learning from Human Feedback (RLHF) and code evaluation tasks. Key Responsibilities AI Code Evaluation & Ranking: Review AI-generated JavaScript/TypeScript code snippets, evaluate their correctness, performance, and readability, and rank competing model responses. Code Prompt Authoring: Write clean, bug-free reference code solutions, unit tests, and technical explanations to serve as high-quality ground-truth training data for AI models. Bug Fixing & Refactoring: Identify syntax errors, logic flaws, and security vulnerabilities in code generated by LLMs, providing correct refactored versions. Guideline & Quality Compliance: Follow technical annotation guidelines to ensure training data meets strict accuracy and quality standards. Feedback & Collaboration: Work closely with AI leads to refine code quality benchmarks and edge-case handling. Qualifications & Skills Experience Level: 0–2 years of experience with JavaScript/TypeScript (bootcamp graduates, CS degree holders, or junior developers welcome). Core JavaScript Skills: Strong understanding of modern JavaScript (ES6+), async programming, DOM manipulation, and core JS principles. Framework Familiarity: Basic familiarity with Node.js, Express, React, or standard testing frameworks (Jest, Mocha) is a plus. Code Quality Focus: Ability to write clean, well-commented code and explain technical concepts clearly in plain English. Attention to Detail: Keen eye for edge cases, performance bottlenecks, and code execution errors. What We Offer Direct experience working on cutting-edge AI code generation models (LLMs/RLHF). Flexible working environment (Remote / Flexible hours). Continuous learning opportunities in AI data engineering and machine learning workflows. Competitive compensation package.