Jonathan Tan is a software engineer in San Jose with 11 years of experience building scalable systems across cloud and data domains, currently contributing at Google after roles at AWS and Cisco. He combines a strong academic grounding in computer science and mathematics with hands-on experience in deep learning frameworks, distributed systems, and production microservices (TensorFlow/Keras, Hadoop, Java Spring, HBase, Snowflake). At AWS he worked on MXNet and benchmarking infrastructure, and at Cisco he automated ETL and cost-saving analytics while prototyping ML models to predict cluster usage and classify server logs. Jonathan’s interests bridge compilers and machine learning, exploring graph-theoretic optimization for compilers and math-driven approaches to model design. Colleagues describe him as curious and systems-minded, comfortable translating research ideas into production tools that reduce latency, cost, and operational risk.
11 years of coding experience
2 years of employment as a software developer
California Polytechnic State University, San Luis Obispo
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