Michael Carilli
Senior Applied Cryptography Engineer at Matter Labs
Albuquerque, New Mexico, United States
Summary
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Rockstar🎓
Top SchoolMichael Carilli is a Senior Applied Cryptography Engineer with 11 years of high-performance computing and GPU expertise, currently building GPU-accelerated zero-knowledge provers for zkSync at Matter Labs to help Ethereum scale securely. He previously spent five years on the PyTorch team at NVIDIA, improving mixed-precision, multi-GPU training and contributing CUDA kernels and distributed training optimizations to the widely used apex repository. His background as a computational scientist and PhD-trained physicist informs a knack for squeezing large speedups from heterogeneous hardware (32x in one CFD project) and adapting numerical methods across GPUs, Xeon Phis, and multicore CPUs. Comfortable moving between low-level CUDA kernels, C++/Fortran interoperability, and cryptographic primitives like multi-scalar multiplication and NTTs, he blends rigorous academic training with practical production engineering. Based in Albuquerque, he pairs deep performance tuning chops with an uncommon focus on cryptography-for-scale, making him effective at turning compute-bound research into deployable blockchain infrastructure.
11 years of coding experience
7 years of employment as a software developer
BS, Physics, 4.0 GPA in major, BS, Physics, 4.0 GPA in major at University of Notre DameDoctor of Philosophy (PhD), Physics, 4.0 GPA, Doctor of Philosophy (PhD), Physics, 4.0 GPA at University of California, Santa Barbara
English, German, Spanish
Stackoverflow
381reputation
11kreached
0answers
14questions
Github Skills (14)
multiprecision10
cuda10
pytorch10
deep-q-learning10
deep-learning10
distributed-training10
optimization10
machine-learning9
kernel9
caching6
cpython6
avx6
filesystem6
python-c-api6
Programming languages (5)
Github contributions (5)
NVIDIA/apex
May 2018 - Aug 2020
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 94 commits, 165 PRs in 2 years 3 months
Contributions summary:Michael primarily contributed to the `apex` repository, a PyTorch extension for mixed precision and distributed training. Their contributions include modifications to the `DistributedDataParallel` module to improve parameter handling, and efficient bucketing and allreduce operations for optimized distributed training. Furthermore, the user made changes to the fused Adam optimizer and related CUDA kernels, indicating expertise in optimizing deep learning training.
distributed-trainingpytorch
mcarilli/pytorch
Oct 2018 - Jun 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:537 pushes, 115 branches in 3 years 8 months
pythongpu-accelerationdeep-learninggpuacceleration
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