Edison Li

Lead applied research efforts focused on large language models, model behavior, evaluation frameworks, and deployment-oriented AI systems. Guide research direction across model usefulness, robustness, evaluation quality, and real-world performance. Partner with research, engineering, product, and safety teams to translate foundation model advances into practical product capabilities. Mentor researchers and applied ML partners across technically ambiguous, high-priority initiatives.

Led applied research projects focused on large language models, evaluation quality, and deployment-oriented machine learning systems. Worked across model experimentation, prompting strategies, retrieval-augmented workflows, and performance analysis in real-world settings. Collaborated with research, engineering, and product teams to improve model usefulness, reliability, and implementation quality. Helped shape evaluation approaches for high-impact initiatives involving model behavior, robustness, and production effectiveness. Contributed to technically complex efforts connecting research progress with product readiness and practical deployment needs.

Led applied machine learning and language-related research supporting large-scale user-facing products Worked across ranking, recommendation, language understanding, and model quality improvement initiatives Collaborated closely with engineering teams on experimentation, offline evaluation, online metrics, and productionization of ML systems Helped strengthen internal research practices around data quality, model validation, and scalable experimentation workflows Provided technical leadership across cross-functional efforts where research decisions carried product, infrastructure, and operational implications Mentored research scientists and machine learning engineers across complex, evolving areas of applied ML

Conducted applied research spanning machine learning, ranking systems, and language technologies. Built and optimized models deployed in large-scale production environments. Partnered with product and engineering teams to translate research into practical solutions. Advanced evaluation methodologies and experimentation practices across high-impact initiatives. Contributed to scalable model development workflows and data-driven decision-making.

Developed and deployed machine learning models for search, personalization, and large-scale data-driven product experiences Worked on applied research problems involving predictive modeling, user behavior analysis, feature engineering, and experimentation Partnered with product and engineering teams to improve model performance, business relevance, and production readiness Contributed during a period when ML workflows were evolving from more classical statistical approaches toward larger-scale learning systems Helped connect applied research output with production product teams in practical, measurable ways