Kevin Chiang is a software development engineer with 10 years of experience building large-scale, low-latency systems and applied ML research, currently working on Ad Supply Path Optimization at Amazon. He blends production engineering—having built a Redis/Netty data fabric that handled 120B queries per day during an A9 internship—with deep research in generative scene representations and robotics from BAIR and a collaboration with OpenAI. His research work re-implemented and extended state-of-the-art models (GQN, Faster-RCNN, PoseCNN) and achieved substantial empirical gains, including a 10x improvement in reconstruction error and SOTA detection from purely simulated data. Comfortable moving between theory and production, he has taught at scale as a head graduate SI for UC Berkeley courses and earned top instructor ratings. Based in the Bay Area with BS/MS degrees from UC Berkeley, he is passionate about the latest AI/ML advances and seeks opportunities that combine impactful engineering with continued learning.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Bachelor of Science - BS at University of California, Berkeley
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Kevin Chiang - Software Development Engineer at Amazon