Matthew Bonanni is a machine learning engineer who leverages a PhD in mechanical engineering and nine years of cross-domain engineering experience to accelerate large language model inference at Red Hat via vLLM. His academic work on massively-parallel large-eddy simulations for rocket and scramjet propulsion gave him deep HPC, C++, and CUDA expertise, and he has repeatedly translated that research into production-grade speedups and models during CBRE fellowships at multiple NASA centers. Prior roles at SpaceX, iRobot, and GE show a practical engineering instinct—designing hardware, automation tools, and test regimes that cut cycle time and boosted reliability. Comfortable at the intersection of high-performance simulation and ML systems, he brings a rare blend of fluid dynamics research, systems programming, and deployment experience to inference optimization.
9 years of coding experience
3 years of employment as a software developer
High School, High School at La Salle Institute
BS, summa cum laude, Mechanical Engineering, GPA 3.99/4.00, BS, summa cum laude, Mechanical Engineering, GPA 3.99/4.00 at Northeastern University
Doctor of Philosophy - PhD, Mechanical Engineering, Doctor of Philosophy - PhD, Mechanical Engineering at Stanford University
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