Payam Siyari is a software engineer in San Francisco with 12 years of experience building data-driven systems for autonomous vehicles and large-scale analytics. He has driven research-grade ML and statistical modeling work at Google and across Uber’s and Aurora’s self-driving programs, translating geo-spatial and weather data into actionable product and operations strategy. His background blends a PhD-level research pedigree in machine learning and statistics with hands-on data engineering (Hive, Spark) and tooling for scenario mining and geo-indexing (H3/UMM). Past roles include inventing scalable pipelines for offline test detection and applying deep learning to satellite and road-network representations, demonstrating both algorithmic depth and production experience. Payam’s international academic foundation and top grades from Sharif and Georgia Tech underpin a knack for turning complex research into pragmatic engineering. Colleagues would note he combines persistence in long-running research problems with a bias for shipping impactful analytics in safety-critical domains.
12 years of coding experience
10 years of employment as a software developer
Master’s Degree, Computer Science - Specialization in Machine Learning, 4.00/4.00, Master’s Degree, Computer Science - Specialization in Machine Learning, 4.00/4.00 at Georgia Institute of Technology
Bachelor of Science (B.Sc.), Computer Science, 18.46/20, Bachelor of Science (B.Sc.), Computer Science, 18.46/20 at Shahid Beheshti University
Master of Science (M.Sc.), Computer Engineering - Software, 19.24/20, Master of Science (M.Sc.), Computer Engineering - Software, 19.24/20 at Sharif University of Technology
Contributions:5 commits, 4 pushes, 1 branch in 5 months
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