LLM Code Trainer / Dataset Quality Reviewer
Reviewed and refined coding prompts, unit tests, and reference solutions for Python, C, JavaScript, and TypeScript to create code-generation datasets for LLM training. Ensured the quality of datasets by clarifying requirements, aligning prompts and tests, and addressing failure modes. Conducted edge-case analyses and validated data through reproducible Docker-based workflows, ensuring adherence to high technical standards.
• Evaluated the technical accuracy and clarity of generated code and unit tests.
• Identified and resolved ambiguities between prompts and test cases.
• Documented requirements, edge cases, and dataset quality concerns.
• Contributed directly to the training and quality of language models through code data curation.
Reviewed and refined coding prompts, unit tests, and reference solutions for Python, C, JavaScript, and TypeScript to create code-generation datasets for LLM training. Ensured the quality of datasets by clarifying requirements, aligning prompts and tests, and addressing failure modes. Conducted edge-case analyses and validated data through reproducible Docker-based workflows, ensuring adherence to high technical standards. • Evaluated the technical accuracy and clarity of generated code and unit tests. • Identified and resolved ambiguities between prompts and test cases. • Documented requirements, edge cases, and dataset quality concerns. • Contributed directly to the training and quality of language models through code data curation.
2026 - Present