Summary
Sobhan Moosavi is a machine learning scientist with a decade of experience building production ML systems for transportation, autonomy, and fraud detection across Uber, Zoox, Lyft, and Atelio by FIS. He holds a PhD in Computer Science from Ohio State and has led high-impact projects such as Lyft’s countrywide routing cost function and Zoox’s Collision Avoidance validation pipelines, blending rigorous research with production engineering. At Atelio he architected a two-tier real-time fraud detection stack that continuously adapts via residual learning, and at Uber he now contributes to Maps, ETA, and routing improvements. Comfortable moving models from prototype to real-time serving, he pairs deep probabilistic and kinematic modeling expertise with large-scale data engineering (Spark/Fugue) and A/B testing. Colleagues rely on him for cross-functional leadership and mentoring, and he often focuses on methodological improvements that reduce data needs and validation costs. Based in San Francisco, he is motivated by applied problems that produce tangible safety and service improvements for users.
10 years of coding experience
11 years of employment as a software developer
Master of Science (M.Sc.), Computer Software Engineering, Master of Science (M.Sc.), Computer Software Engineering at University of Tehran
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Shahid Beheshti University
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at The Ohio State University
English, Persian