Michael Le Gore
About Michael Le Gore
Michael Le Gore is a Staff Software Engineer with extensive experience in backend development and machine learning. He has worked at notable companies such as Microsoft and Sift, and holds a Bachelor of Science in Computer Science from the University of Virginia.
Current Role at Sift
Michael Le Gore currently serves as a Staff Software Engineer at Sift, a position he has held since 2021. In this role, he focuses on backend development for high-scale web services that cater to hundreds of millions of users. He leads the Content team, where he is responsible for building machine learning models and engineering features that enhance content moderation. His work contributes significantly to the company's mission of providing secure and efficient online experiences.
Previous Experience at Sift
Prior to his current position, Michael Le Gore worked at Sift as a Senior Software Engineer from 2019 to 2021 and as a Software Engineer from 2018 to 2019. During his tenure, he engaged in various projects that involved machine learning and content moderation. His contributions during these years helped shape the company's technological advancements in handling online content.
Experience at Microsoft
Michael Le Gore has a background in software engineering from his time at Microsoft, where he worked from 2013 to 2017 as a Software Engineer II. He also completed an internship as an Xbox Live Software Intern from 2011 to 2012. His experience at Microsoft provided him with a strong foundation in software development and engineering practices.
Education and Academic Background
Michael Le Gore earned a Bachelor of Science (B.S.) in Computer Science from the University of Virginia, where he studied from 2009 to 2013. He also attended Thomas Jefferson High School for Science and Technology from 2005 to 2009. During his studies, he served as a Research Assistant at the University of Virginia for five months in 2010, gaining valuable research experience.
Projects and Interests
Michael Le Gore engages in projects related to home automation and machine learning. He has a keen interest in developing systems for music discovery and recommendation. Additionally, he contributes to initiatives aimed at democratizing the web through peer-to-peer decentralized systems, reflecting his commitment to innovative technology solutions.