Jonny Li
About Jonny Li
Jonny Li is a Machine Learning Engineer II at SoundHound AI in Toronto, Ontario, Canada, where he has worked since 2023. He has a background in software engineering and holds a Bachelor's degree in Computer Science and Linguistics from the University of Toronto.
Work at SoundHound AI
Jonny Li currently holds the position of Machine Learning Engineer II at SoundHound AI, where he has been employed since 2023. In this role, he focuses on developing advanced machine learning solutions. Prior to this position, he worked as a Software Engineer at SoundHound AI from 2021 to 2023. His contributions during this time included the development of a distributed LLM inference server and the implementation of various data and model pipelines.
Education and Expertise
Jonny Li earned a Bachelor's degree in Computer Science and Linguistics from the University of Toronto, where he studied from 2015 to 2021. He also participated in a Foreign Exchange program at The University of Tokyo from 2018 to 2019. His educational background provides a strong foundation in both computational techniques and linguistic analysis, which supports his work in machine learning.
Background in Software Development
Before joining SoundHound AI, Jonny Li gained experience as an SDE Intern at Amazon in 2020, where he worked for two months. His internship provided him with practical exposure to software development in a large-scale environment. This experience, combined with his previous roles, has equipped him with a diverse skill set in software engineering and machine learning.
Machine Learning Projects
Jonny Li has developed a distributed LLM inference server utilizing Python, Rust, and Kubernetes. He has also implemented data, model, training, and hyperparameter tuning pipelines using PyTorch. Additionally, he created a latency and throughput load testing framework for distributed systems, showcasing his ability to handle complex machine learning tasks.
Research in Neural ASR Models
Jonny Li has conducted research on domain adaptation techniques specifically for neural automatic speech recognition (ASR) models. This research adds to his expertise in machine learning and highlights his focus on improving the performance of speech recognition systems.