Being part of the Machine Learning team at Amazon is one of the most exciting engineering job opportunities in the world today. Our Machine Learning (ML) team is comprised of technical leaders with different backgrounds who create and develop novel and infinitely-scalable applications that optimize Amazon’s systems using cutting edge machine learning techniques. We develop innovative algorithms that model patterns within data to drive automated decisions at scale in all corners of the company, including our e-Commerce site and subsidiaries, Amazon Web Services, Seller & Buyer Services and Digital Media including Kindle. In this talk, I will give you an overview of some of the technical challenges we face and I will present some examples of ML-based solutions that had a significant impact inside the company.
Glenn Fung received a B.S. in pure mathematics from Universidad Lisandro Alvarado in Barquisimeto, Venezuela. He then earned an M.S. in applied mathematics from Universidad Simon Bolivar, Caracas, Venezuela, where later he worked as an assistant professor for two years. He also earned an M.S. degree and a Ph. D. degree in computer sciences from the University of Wisconsin-Madison. His main interests are optimization approaches to machine learning and data mining, with emphasis in kernel methods. In the summer of 2003 he joined the computer aided diagnosis group at Siemens Healthcare in Malvern, PA where he worked for 10 years developing and applying novel machine learning techniques to solve challenging problems that arise in the medical domain. In September of 2013 he joined the Amazon where he has been working on applying novel machine learning techniques to solve challenging problems that arise in e-commerce retail.
lwehbe [atsymbol] cs.cmu.edu