Przemysław Wysocki
AI Frameworks Engineer at Intel Corporation
Warsaw, Masovian Voivodeship, Poland
Summary
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Rockstar🎓
Top SchoolPrzemysław Wysocki is an AI Frameworks Engineer with six years of experience building and optimizing deep learning inference tooling, currently working on Intel’s OpenVINO team in Warsaw. He combines a formal background in Automatic Control and Robotics with a Master’s in Machine Learning to bridge mechanical systems intuition and production-grade ML engineering. At Intel he owns a cross-platform Python codebase for numerically comparing inference backends, contributes C++/Python bindings, and represents Intel in the ONNX community—having implemented and tested ONNX operators and added device-configuration snippets used in OpenVINO. Comfortable across CI, Docker, and multi-OS environments, he’s also contributed practical fixes and type-conversion utilities to high-profile open-source projects. Colleagues describe him as a detail-oriented engineer who pairs low-level implementation work with system-level architectural thinking.
6 years of coding experience
1 year of employment as a software developer
Master of Engineering - MEng, Machine Learning, Master of Engineering - MEng, Machine Learning at Warsaw University of Technology
English, Polish
Github Skills (11)
machine-learning10
deep-learning10
onnx10
python10
openvino10
numpy10
deep-neural-networks9
neural-network9
computer-vision9
pytorch8
tensorflow8
Programming languages (6)
Github contributions (5)
openvinotoolkit/openvino
Mar 2022 - Jan 2023
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Role in this project:
Back-end Developer
Back-end DeveloperContributions:851 reviews, 33 commits, 227 PRs in 10 months
Contributions summary:Przemysław contributed to documentation and Python snippets for configuring devices, addressing bug fixes, and making minor code changes to the OpenVINO toolkit. They also added Python bindings for cloning functions, including tests and code style updates, co-authoring the commits with other developers. Furthermore, the user added a method for converting OpenVINO types to NumPy dtypes, and created a test with different NumPy types.
inference-enginepytorchmodel-optimizerdeep-learninggpu
onnx/onnx
Oct 2022 - Jan 2023
Open standard for machine learning interoperability
Role in this project:ML Engineer
ML EngineerContributions:1 release, 46 reviews, 8 commits in 3 months
Contributions summary:Przemysław's primary focus was on extending and improving the ONNX library, a standard for machine learning interoperability. Their contributions involved implementing and refining operators, specifically related to the Split and LpPool operations, and included adding new attributes (e.g., `num_outputs`, `dilations`). They also updated existing tests and added new ones to ensure the functionality and correctness of the implemented changes. The user's work is directly related to enhancing ONNX's capabilities in areas such as tensor manipulation and pooling operations.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
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