Summary
Daniel Snider is a Compiler Software Engineer with 13 years of experience building scalable systems at the intersection of ML, high-performance computing, and production infrastructure. Currently at AWS working on Trainium, he pairs compiler and hardware-aware optimization expertise with PhD-level research from the University of Toronto focused on ML compilers, distributed ML systems, and profile-guided, multi-scale performance profiling. His background includes leading large-scale medical data and computer vision platforms at SickKids that processed 100M images and indexed billions of metadata entries, plus robotics autonomy and cloud-infrastructure roles that span C++, Python, ROS, OpenStack, and distributed services. He brings a rare blend of research-published innovations and hands-on production delivery, able to translate profiling insights into compiler and system-level speedups for real-world ML workloads.
13 years of coding experience
8 years of employment as a software developer
Computer Science, Computer Science at Freie Universität Berlin
Bachelor of Information Technology, Bachelor of Information Technology at Ontario Tech University
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at University of Toronto
Machine Learning @ Stanford University, Machine Learning @ Stanford University at Coursera.org