Diego Fiori
CTO & Co-Founder at Nebuly
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDiego Fiori is a CTO and co-founder based in San Francisco with seven years of hands-on experience building and shipping AI-driven products. Trained as a physicist at EPFL and Politecnico di Torino, he blends rigorous quantitative thinking with practical machine learning engineering to take models from research to production across cloud environments. At Nebuly he leads efforts to simplify the complexity of LLM-powered products, while his past work at L2F shows end-to-end delivery of AutoML, NLP, and computer vision systems for healthcare, insurance, and logistics. He contributes to open-source AI tooling—most notably performance optimizations for Hugging Face models using ONNX, OpenVINO, and TensorRT—demonstrating a focus on inference acceleration and cross-runtime reliability. Curious and product-minded, Diego pairs low-level model optimization skills with a founder’s emphasis on real user behavior and deployable workflows.
7 years of coding experience
3 years of employment as a software developer
Master of Science - MS Physics, Master of Science - MS Physics at EPFLBachelor's degree Engineering Physics/Applied Physics, Bachelor's degree Engineering Physics/Applied Physics at Politecnico di Torino
Italian, French, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
tensorrt10
pytorch10
huggingface10
deep-learning10
onnx10
openvino10
llm9
large-language-models9
lm9
ai8
Programming languages (6)
Github contributions (5)
nebuly-ai/optimate
Feb 2022 - Jan 2023
A collection of libraries to optimise AI model performances
Role in this project:
ML Engineer
ML EngineerContributions:21 releases, 103 reviews, 24 commits in 11 months
Contributions summary:Diego primarily contributed to optimizing AI model performance within the `optimate` repository. Their work involved integrating and refining Hugging Face models, along with ONNX, OpenVINO, and TensorRT optimization techniques. Key efforts include implementing a Hugging Face API, addressing errors in various runtime environments (ONNXRuntime, OpenVINO, TensorRT), and modifying model behaviors for CPU and GPU utilization, indicating a focus on inference acceleration.
pypicompilertensorflowtransformersedge-computing
diegofiori/benchmarks
Mar 2022 - Mar 2023
Contributions:179 pushes, 1 branch in 1 year
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