Freelance AI Math Trainer & Annotator (DataAnnotation.tech)
Evaluated and ranked AI-generated mathematical responses across calculus, linear algebra, statistics, and discrete mathematics using multi-dimensional quality frameworks. Applied accuracy, reasoning, truthfulness, and consistency standards to improve training data quality for advanced language models. Identified logical errors, mathematical inaccuracies, and edge-case failures, strengthening dataset reliability and reducing annotation inconsistencies.• Conducted structured reasoning assessments to support model alignment and improve mathematical problem-solving performance.• Maintained high-quality review standards for complex quantitative and reasoning-intensive tasks.• Contributed to the development of high-integrity datasets for large-scale AI training initiatives.• Performed large-volume quality review and annotation to ensure dataset correctness and consistency.