Fionn Malone
Software Engineer At Google Quantum AI
California, United States
Summary
🤩
Rockstar🎓
Top SchoolFionn Malone is a software engineer at Google Quantum AI with 12 years of experience applying theoretical physics and high-performance numerical methods to quantum many-body problems. He earned a PhD in Physics from Imperial College London after topping his class in theoretical physics at Trinity College Dublin, and has transitioned those analytic skills into production-grade software and GPU-optimized kernels. His open-source work on QMCPACK includes implementing batched matrix operations and optimizing linear algebra kernels for AFQMC, reflecting a strong focus on numerical algorithms and performance. At Google and prior roles at LLNL and QC Ware, he has bridged research and engineering to make advanced Quantum Monte Carlo methods practical at scale. Colleagues rely on him for both rigorous theory and hands-on code that squeezes performance from modern hardware. He is based in California and brings a rare combination of deep physics intuition and production systems engineering to quantum software.
12 years of coding experience
7 years of employment as a software developer
BA, Theoretical Physics, First Class Honours. Ranked first in class., BA, Theoretical Physics, First Class Honours. Ranked first in class. at Trinity College, DublinDoctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at Imperial College London
English
Github Skills (5)
c-language10
cprogramming-language10
performance-optimization10
linear-algebra10
fortran9
Programming languages (4)
Github contributions (5)
QMCPACK/qmcpack
Oct 2018 - Dec 2020
Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 672 commits, 123 PRs in 2 years 3 months
Contributions summary:Fionn contributed to the implementation of batched matrix operations and improvements to the performance of the code. This involved adding and optimizing key linear algebra kernels. The primary focus of the contributions was on numerical algorithms and performance within the AFQMC framework.
quantum-monte-carlompiatomsgpu-supportc-plus-plus
fdmalone/ReCirq
May 2024 - Jul 2024
Research using Cirq!
Contributions:84 pushes, 31 branches in 2 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.