Yifeng Zhang
About Yifeng Zhang
Yifeng Zhang is a Senior Software Engineer with extensive experience in distributed systems and big data infrastructure. He has worked for notable companies such as Microsoft, Tencent, Baidu, Amazon, and currently Cerebras Systems, and holds a Master's degree in Computer Engineering from the University of Florida.
Work at Cerebras Systems
Yifeng Zhang currently holds the position of Senior Software Engineer at Cerebras Systems, where he has been employed since 2022. In this role, he focuses on distributed systems and big data infrastructure. His expertise includes scaling out large language model distributed training using Kubernetes, which is essential for optimizing performance in high-demand computing environments.
Previous Experience in Software Engineering
Before joining Cerebras Systems, Yifeng Zhang accumulated extensive experience in software engineering across several prominent technology companies. He worked at Amazon as a Software Engineer from 2020 to 2022 in Vancouver, British Columbia, Canada. Prior to that, he served as a Senior Software Engineer at Baidu, Inc. from 2015 to 2020 in Shanghai City, China. His career began at Tencent, where he was a Software Engineer from 2013 to 2015, also in Shanghai. Additionally, he briefly worked at Microsoft in 2015 for seven months in Suzhou, Jiangsu, China.
Education and Expertise
Yifeng Zhang holds a Master's degree in Computer Engineering from the University of Florida, which he completed in 2012. He also earned a Bachelor's degree in Electrical and Computer Engineering from Southeast University in 2011. His educational background supports his specialization in distributed systems and big data infrastructure, particularly in the context of large language model distributed training.
Technical Specializations
Yifeng Zhang specializes in scaling out large language model distributed training using Kubernetes. His technical expertise includes working with infrastructure that operates at the scale of thousands of nodes. This specialization is crucial for developing efficient and scalable solutions in the field of distributed systems.