Max Gilchrist
About Max Gilchrist
Max Gilchrist is a Machine Learning Engineer currently working at Blaize in Cary, North Carolina. He specializes in edge computing solutions and has contributed significantly to the development of AI Studio, an edge AI platform.
Current Role at Blaize
Max Gilchrist has been employed at Blaize as a Machine Learning Engineer since 2021. In this role, he focuses on the architectural design of Studio’s Edge AI, demonstrating his expertise in edge computing solutions. His responsibilities include designing, maintaining, and debugging components related to Edge ML operations. Gilchrist also serves as the code owner for the Philippines team's edge server code, which facilitates the deployment of edge solutions for AI Studio.
Previous Experience at IBM
Before joining Blaize, Max Gilchrist worked as a Data Science Intern at IBM in 2019 for a duration of three months. This internship took place in the Greater Atlanta Area and provided him with foundational experience in data science, contributing to his skill set in machine learning and data analysis.
Educational Background
Max Gilchrist studied at the University of California, Berkeley, where he earned a Bachelor's degree in Applied Mathematics and Computer Science. His academic journey spanned from 2017 to 2021, equipping him with a solid foundation in mathematical principles and computational techniques that support his work in machine learning.
Internship Experience at Blaize
Prior to his current position, Max Gilchrist completed a Machine Learning Engineering Internship at Blaize in 2020. This internship lasted for three months and took place in Cary, North Carolina. During this time, he gained practical experience that contributed to his development as a machine learning engineer.
Contributions to AI Studio
Max Gilchrist played a significant role in the development of AI Studio, an edge AI platform. He provided thought leadership in the design of a REST API for AI Studio, enabling effective communication with the newly integrated Edge Machine Learning Server. Additionally, he contributed to optimizing GStreamer pipelines to improve the display of inference results from Edge Machine Learning devices.