Summary
Peter Lillian is a founder and machine learning engineer with a decade of experience building ML systems from research prototypes to hardware-accelerated production workloads. Based in San Francisco, he has led hardware ML research on FPGA-based neuroevolution and scaled training and inference across GroqChips, with hands-on expertise in quantization (QAT/PTQ), reinforcement learning, and evolutionary algorithms. He founded two startups—one that staged B2B online trade shows and a stealth startup launched in 2024—demonstrating a pattern of turning research insight into product. His academic work includes leading a team to evolve FPGA architectures as universal function approximators and publishing ML/robotics research, reflecting a rare blend of neuroscience-inspired theory and systems-level engineering. Collected skills span neural networks, neuroevolution, hardware acceleration, and customer-facing model optimization for latency and throughput.
10 years of coding experience
8 years of employment as a software developer
Master of Science - MS Electrical Engineering Machine Learning, Master of Science - MS Electrical Engineering Machine Learning at University of Southern California