West Covina, California, United States
Master’s-trained mathematician passionate about solving complex problems through data, research, and technology. Currently a Quality & Policy Expert and Senior Data Labeling Analyst supporting Meta, improving data integrity and LLM training workflows through cross-functional collaboration. Driven by curiosity, innovation, and a commitment to building impactful, data-driven solutions.
Lead cross-functional communication between Data Quality Analysts (DQAs) and Data Labeling Analysts (DLAs) to improve labeling accuracy and guideline clarity Develop and standardize documentation to operationalize LLM training and improve label consistency Analyze quality performance insights and recommend improvements to visualization tools and qualitative feedback processes
Annotate and validate large-scale datasets to support LLM training and classification workflows Conduct frequency and quality analysis to identify inconsistencies and improve labeling accuracy Provide structured feedback to refine guidelines and enhance future model training performance
Applied labeling guidelines to structured and unstructured datasets with high accuracy Performed data audits and corrections to ensure quality and consistency Supported workflow documentation and reporting for cross-team collaboration
Create, analyze, and revise training prompts and solutions in various mathematical fields to enhance the performance of Large Language AI models developed by the Snorkel startup company
Designed and evaluated advanced mathematical prompts to improve the performance of LLMs, including ChatGPT and Google Gemini Analyzed solution accuracy and logical structure to strengthen model reasoning capabilities Contributed to training data development for state-of-the-art generative AI systems
Label raw data to be utilized as training data for AI and machine learning models designed and tested by AirBox.