AI Training Data & Code Annotation for LLM Optimization (xAI)
Worked as a Frontend Developer Specialist at xAI, contributing to AI training data pipelines focused on improving large language model performance on code-related tasks. My work involved annotating, reviewing, and refining large volumes of frontend and full-stack code (JavaScript, TypeScript, React) to ensure high-quality training datasets. I focused on identifying incorrect logic, improving code readability, and aligning outputs with best practices and real-world developer standards. In addition to annotation, I performed quality checks on AI-generated code, flagged inconsistencies, and helped define guidelines for structured labeling. I collaborated closely with AI researchers and engineering teams to improve model accuracy in areas like code generation, debugging, and refactoring. This required strong attention to detail, analytical thinking, and a deep understanding of modern frontend architectures, ensuring the training data was both accurate and practically useful.