Summary
Azar Fazel is a Senior Autonomy Engineer with 12 years of experience building machine learning and computer vision systems for wearable devices and autonomous driving. He holds a Master’s in Electrical and Electronics Engineering focused on Machine Learning and AI from Stanford and a BSc in Computer Engineering. Azar’s career spans research-built assistive tech at Stanford (including the Autism Glass project) to production ML and perception work at NIO, Amazon, and now Glydways, demonstrating a track record of moving models from lab prototypes to deployed systems. He combines strong statistical analysis and algorithmic skills with hands-on engineering for embedded and vehicle-scale platforms. Notably, his background bridges neuroinformatics and driving perception—showing an ability to translate biological data analysis approaches into robust real-time vision solutions. Based in the United States, he thrives on tackling safety-critical autonomy problems where ML meets constrained hardware.
11 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Isfahan University of Technology
Master's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at Stanford University
English, Persian