Matthias Reso
Applied AI at Meta
San Jose, California, United States
Summary
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Rockstar🎓
Top SchoolMatthias Reso is an Applied AI engineer based in San Jose with five years of hands-on experience building and productionizing machine learning systems at scale. At Meta he helps advance PyTorch as a leading ML framework, bringing prior experience optimizing distributed training and benchmarking from Samsung SDS where he achieved dramatic speedups on ResNet and DistilBERT workloads. His background spans research and productization of computer vision models—holding a PhD in Computer Vision—and includes end-to-end deployment work with Docker, orchestration and data pipelines. An active open-source contributor, he has improved benchmarking and robustness in high-profile projects like pytorch/serve and contributed tests and tooling to the meta-llama cookbook, showing attention to reproducibility and code quality. Notably, he combines deep research experience with practical MLOps skills to shave minutes off large-scale training runs and make models reliably deployable.
5 years of coding experience
9 years of employment as a software developer
Diplom, Informationstechnik, Diplom, Informationstechnik at Universität PaderbornDoctor of Philosophy - PhD, Computer Vision, Doctor of Philosophy - PhD, Computer Vision at Leibniz Universität Hannover
Stackoverflow
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Github Skills (17)
pytorch10
benchmark10
pytest10
python10
testing10
machine-learning10
benchmarking10
mlops10
fine-tuning10
optimization10
kubernetes9
dockers9
llm9
docker9
kubernetes-pods9
Programming languages (5)
Github contributions (5)
meta-llama/llama-cookbook
Aug 2023 - Feb 2025
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 95 reviews, 88 PRs in 1 year 5 months
Contributions summary:Matthias's commits primarily focus on the development and improvement of machine learning recipes and related scripts within the Llama Cookbook repository. They addressed a crucial bug by fixing a division-by-zero error in the training utility, updated imports for improved code organization, and added a unit test for the training method, demonstrating a focus on code quality. The contributions also include adding support for torchrun and adjusting the dataset configurations, indicating a solid understanding of the project's machine learning focus and related infrastructure.
aifinetuninglangchainllamallama2
pytorch/serve
Feb 2022 - Jan 2023
Serve, optimize and scale PyTorch models in production
Role in this project:MLOps Engineer
MLOps EngineerContributions:325 reviews, 41 commits, 164 PRs in 11 months
Contributions summary:Matthias's commits primarily focus on improving the benchmark script for the PyTorch Serve repository. They removed unnecessary logs and global variables to optimize the benchmark process. Furthermore, the user implemented and integrated Locust and addressed general code refactoring to enhance the benchmark's capabilities. Their work contributed towards generating more robust and versatile benchmarking reports.
cpupytorchpytorch-modelsservingin-production
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