David Kogan
About David Kogan
David Kogan is an incoming Software Engineer Intern at Nuro, specializing in ML Ops and focusing on the operationalization of machine learning models. He has prior experience as a Machine Learning Engineer Intern at the Michael G. DeGroote Institute for Infectious Disease Research and Filament AI, and has also worked as a Software Engineer Intern at Broadridge.
Work at Nuro
David Kogan is currently employed as an Incoming Software Engineer Intern at Nuro, a position he has held since 2022. Nuro specializes in autonomous delivery solutions, and Kogan's role involves contributing to the development and operationalization of machine learning models. His work aligns with Nuro's mission to enhance delivery services through innovative technology.
Previous Experience in Machine Learning
Before joining Nuro, Kogan gained valuable experience as a Machine Learning Engineer Intern at the Michael G. DeGroote Institute for Infectious Disease Research from 2020 to 2021. He worked there for 11 months, focusing on machine learning applications in a research setting. Additionally, he interned at Filament AI for 3 months in 2021, further developing his skills in machine learning.
Software Engineering Background
Kogan's background in software engineering includes an internship at Broadridge in 2019, where he worked for 3 months. This experience provided him with insights into software development processes and practices. His diverse internships have equipped him with a comprehensive understanding of both software engineering and machine learning.
Education and Expertise
David Kogan studied Computer Engineering at McMaster University, where he earned his Bachelor of Engineering degree from 2018 to 2023. He also completed his secondary education at Richmond Hill High School in Ontario from 2014 to 2018. His academic background has laid a strong foundation for his expertise in machine learning operations (ML Ops) and software engineering.
Aspirations in Technology
Kogan aspires to bring positive change through technology, demonstrating a commitment to impactful tech solutions. His motivation is reflected in his choice of internships and projects, focusing on the operationalization of machine learning models to address real-world challenges.