Petros Mol is a Staff Software Engineer based in New York with nine years of industry experience and a PhD in computer science, specializing in large-scale machine learning systems. He researches and ships transformer-based foundation models for time-series forecasting and anomaly detection, taking models from novel architectures through experiments to production at Google scale. His work has materially reduced congestion in Cloud Data Center Networks and delivered the largest quality improvement to Ads campaign forecasting in his project history. Earlier research built scalable kernel methods and distributed SVMs integrated into TensorFlow, reflecting deep quantitative roots alongside production engineering rigor. Petros thrives where rigorous mathematical thinking meets messy real-world data—financial modeling, event prediction, and resource optimization are natural fits. Outside of product metrics, his background in cryptography and lattice analysis gives him an uncommon perspective on model robustness and system security.
9 years of coding experience
10 years of employment as a software developer
University of California, San Diego
Bachelor / MEng, Electrical and Computer Engineering, Bachelor / MEng, Electrical and Computer Engineering at National Technical University of Athens
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