Summary
Rajath Koratagere Manjunath is a software engineer based in San Diego with eight years of experience building perception, planning, and control systems for self-driving cars and robotics. His work bridges research and production—ranging from a neural, field-controlled motion planner developed at NYU that cut planning computation by 90% for a 7DoF robot to vehicle perception stacks using LaneNet and YOLO. He has applied these skills in industry roles at XPENG and Amazon, focusing on integrating computer vision with motion planning and model predictive control. Rajath combines electrical engineering roots and hands-on hardware experience (Arduino, XBee, drone stabilization) with deep learning and algorithmic research, making him effective at moving ideas from prototype to deployed systems. An enthusiast for robotics and AI, he favors practical, computation-efficient approaches that preserve solution quality while drastically reducing runtime.
8 years of coding experience