Liang Tan

Liang Tan is a highly experienced machine learning engineer with a robust educational background, holding a degree from Tulane University. Tan began as a Teaching Assistant for the Introduction to Numerical Methods for Chemical Engineering course before transitioning to a Research Assistant role focused on surfactant-oil-water systems and data management. Tan’s career progressed through a position as a Machine Learning Research Scientist at Stratifyd, followed by significant roles at Meta where responsibilities included leading design efforts for various machine learning services and frameworks, such as the IDEA service and the FAIM library. Tan has extensive experience with natural language processing, generative AI, and cross-team collaborations, along with mentoring in machine learning infrastructure and algorithm implementation.