Jeff Gee
About Jeff Gee
Jeff Gee is a software engineer with a Master of Science from Carnegie Mellon University. He specializes in the deployment and maintenance of machine learning systems and has worked for companies such as LinkedIn and Handshake.
Work at Handshake
Jeff Gee has been employed at Handshake as a Software Engineer since 2022. In this role, he focuses on the deployment, automatic retraining, and health monitoring of machine learning models. His work involves ensuring the reliability and efficiency of the systems that support these models, contributing to the overall performance of the organization's machine learning initiatives.
Previous Experience at LinkedIn
Jeff Gee worked at LinkedIn as a Software Engineer for a total of four years, from 2015 to 2022. His tenure included two distinct periods: first from 2015 to 2018, and then from 2018 to 2022. During his time at LinkedIn, he was involved in various projects that enhanced his skills in software engineering and machine learning.
Education and Expertise
Jeff Gee holds a Master of Science (MS) degree from Carnegie Mellon University, where he specialized in areas related to machine learning and software engineering. He also earned a Bachelor of Science (BS) from UC Irvine. Additionally, he studied as a Foreign Exchange Researcher at the Tokyo Institute of Technology, focusing on Speech Recognition.
Background in Software Engineering
Jeff Gee began his career at Canon Information & Imaging Solutions, where he held various positions, including Associate Software Engineer from 2009 to 2010 and Software Engineer from 2010 to 2013. He also gained experience as a Graduate Researcher at Carnegie Mellon University’s Language Technologies Institute and as a Graduate Intern at IBM Watson in 2014.
Focus on Machine Learning Systems
Jeff Gee has a keen interest in solving problems related to the deployment and maintenance of machine learning systems. His expertise extends beyond model development to include the operational aspects of machine learning, such as automatic retraining and health monitoring of models and their datasets.