About

William Zeng is a highly skilled Machine Learning Engineer at Google with a focus on developing deep recommendation systems. With over 3.79 years of relevant experience, he has a proven track record of designing, implementing, and optimizing complex systems that leverage machine learning algorithms to deliver personalized recommendations to users. In his current role as a Software Engineer at Google, William is responsible for developing and maintaining deep learning recommendation systems that power some of the most popular Google products. He works closely with product managers, data scientists, and other engineers to ensure that these systems are performing optimally and delivering the best possible user experience. William's past experience at Google includes two internships where he designed and implemented various features and systems. During his first internship, he worked on a match scoring feature that calculates the fit between a person and job, and implemented several API endpoint admin tools for the Cloud Profile Discovery private beta release. During his second internship, he designed and implemented a collaborative filtering job recommendation system in Google Cloud Jobs API, created a MapReduce data pipeline that analyzes user event data, and exposed the recommendation API as part of Cloud Jobs API. William holds a Master's degree in Computer Science from Stanford University, where he specialized in machine learning and deep learning. He also holds a Bachelor of Science (B.S.) in Computer Science from the same institution. Prior to his graduate studies, he attended Wayzata High School. William's technical skills include expertise in recommendation systems, machine learning, software engineering, deep learning, MapReduce, and testing. He is a highly motivated and detail-oriented professional who is passionate about leveraging technology to solve complex problems and deliver innovative solutions.