Summary
Majid Moghadam is an AI research scientist in California with 8 years of experience building deep learning and reinforcement learning systems for autonomous driving and robotics. He combines a strong academic background (PhD work at UC Santa Cruz and Istanbul Technical University) with industry impact at Meta and Audi, delivering perception, trajectory planning, and ADAS pipelines using Python, PyTorch, and C++. His research contributions include continuous deep RL for vehicle trajectory planning, hierarchical ADAS frameworks tested in CARLA, and open-source simulators and tools that attracted community interest. He has hands-on experience across the sensor stack—LiDAR/pointcloud 3D detection, radar-lidar-camera fusion, and motion prediction/tracking—paired with classical control knowledge from aerospace and UAV projects. Notably, he translated research into production-focused tooling such as automated HD-map evaluation pipelines and scenario tagging for fleet data. He is drawn to problems at the intersection of learning-based perception, decision-making, and robust real-world deployment.
8 years of coding experience
7 years of employment as a software developer
Bachelor’s Degree, Electrical and Electronics Engineering, Bachelor’s Degree, Electrical and Electronics Engineering at Azad University (IAU)
University of California Santa Cruz
PhD Student (Transferred to UCSC), Aerospace, Aeronautical and Astronautical Engineering, 3.94/4.0, PhD Student (Transferred to UCSC), Aerospace, Aeronautical and Astronautical Engineering, 3.94/4.0 at Istanbul Technical University
English, Turkish, Persian, Azerbaijani