Gordon Gustafson
About Gordon Gustafson
Gordon Gustafson is a Principal Engineer Team Lead for the ML Training Platform at Motional, where he has worked since 2023. He has extensive experience in software engineering, having held various roles at companies such as Hulu and Explorys, and has a Bachelor of Science in Computer Science from Cornell University.
Current Role at Motional
Gordon Gustafson currently serves as Principal Engineer Team Lead for the ML Training Platform at Motional. He has held this position since 2023, working remotely. In this role, he manages a team of engineers and oversees the development of machine learning training solutions. His leadership focuses on enhancing the efficiency and effectiveness of ML training processes.
Previous Experience at Motional
Prior to his current role, Gustafson worked at Motional in various capacities. He was a Senior Engineer Team Lead for the ML Training Platform from 2022 to 2023, and before that, he served as a Senior Software Engineer on the same platform from 2021 to 2022. His tenure at Motional began in 2020 as a Software Engineer, where he contributed to the development of machine learning applications.
Experience at Hulu
Gustafson worked at Hulu from 2018 to 2020, where he held multiple roles. He was a Software Engineer focused on the Ad Platform for two years. Additionally, he served as a Software Engineer in DevOps from 2017 to 2018 and completed several internships at Hulu, including a DevOps Intern and Core Services Software Intern in 2016 and 2015, respectively.
Educational Background
Gustafson studied at Cornell University, where he earned a Bachelor of Science (BS) degree in Computer Science from 2013 to 2017. His education provided a foundation in software engineering and computer science principles, which he has applied throughout his career in various engineering roles.
Key Contributions and Achievements
Throughout his career, Gustafson has made significant contributions to machine learning and software engineering. He drove a cross-team project that reduced the time and cost to train on new data by 50%. He also managed a team of four engineers to build an ML Training Platform utilized by 100 ML engineers and saved $25,000 per month in AWS costs through optimization efforts.