Deep Doshi is a Segment Technology Lead at Valeo with 9 years of experience building perception and autonomy systems for self-driving vehicles, specializing in Vision-Language Models, multi-sensor fusion, and 3D mapping using NeRF and Gaussian splatting. He blends research-driven techniques—self-supervised learning and transformer architectures—with production engineering in C++ and Python, shipping SLAM, object tracking, and contextual perception features across automotive platforms. His background spans hands-on robotics work from startups to industry, including audio localization, hand-gesture traffic control, and mobile robot autonomy, giving him a practical edge in sensor-constrained environments. Deep mentors and reviews student projects for Udacity, reflecting a commitment to growing the next generation of roboticists. Based in California, he brings a rare combination of foundational AI model work and vehicle-grade systems experience that accelerates turning multi-modal research into deployable automotive capabilities.
9 years of coding experience
5 years of employment as a software developer
Master of Science (M.S.) Electrical and Electronics Engineering, Master of Science (M.S.) Electrical and Electronics Engineering at Michigan Technological University
Self Driving Car Nanodegree Robotics Nanodegree, Self Driving Car Nanodegree Robotics Nanodegree at Udacity
Shri P.V. Modi School
St. Mary's School
Bachelor's degree Mechatronics Engineering, Bachelor's degree Mechatronics Engineering at Ganpat University
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