Prerit Jaiswal is a founding scientist and AI research engineer with a PhD in theoretical physics and a decade of experience applying deep learning to real-world products, from voice AI for drive-throughs to autonomy-focused HD mapping. He has moved fluidly between research and product roles at startups and large tech—designing deep learning models for Amazon Chime, building cashierless checkout ML at Standard Cognition, and contributing core perception work for self-driving challenges and DeepMap. His background in theoretical particle physics honed rigorous modeling and simulation skills that he now leverages to design discriminative features and robust ML pipelines in production. Based in Fremont, CA, he blends academic rigor with hands-on engineering across C++, Python, TensorFlow and ROS, and has a particular enthusiasm for self-driving systems that shows in both project choice and specialized nanodegrees. Notably, he has a track record of translating analytic insight into corrected experimental practice—work that once led LHC collaborations to update their Monte Carlo analyses.
10 years of coding experience
12 years of employment as a software developer
Indian Institute of Technology Bombay
Deep Learning, Deep Learning at Udacity
PhD, Physics, PhD, Physics at Stony Brook University
Contributions:142 commits, 114 pushes, 1 branch in 6 months
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