Zuzanna Gawrysiak
AI Engineer at Vestigit
Greater Poland Voivodeship
Summary
🤩
Rockstar🎓
Top SchoolZuzanna Gawrysiak is an AI Engineer with five years of experience building and optimizing deep learning systems across industry and research-focused roles. She has contributed to high-performance ML frameworks like PaddlePaddle—implementing and refactoring quantization for core operators in C++—and has hands-on experience deploying efficient inference backends. Her background includes roles at Intel and TerraEye and she now works at Vestigit while pursuing advanced studies in artificial intelligence, signaling a blend of production engineering and academic rigor. Known for attention to low-level performance details, she bridges model-level research and systems-level optimization to make ML workloads faster and more deployment-ready.
4 years of coding experience
3 years of employment as a software developer
PhD, Artificial Intelligence, PhD, Artificial Intelligence at Politechnika PoznańskaMaster of Science (MSc), Artificial Intelligence, 4.9/5.0, Master of Science (MSc), Artificial Intelligence, 4.9/5.0 at Poznan University of Technology
Polish, English, German
Github Skills (12)
neural-network10
quantization10
paddlepaddle10
machine-learning10
deeplearning-ai10
c-language10
deep-learning10
cprogramming-language10
efficientnet10
python9
distributed-training9
tensorflow4
Programming languages (3)
Github contributions (5)
PaddlePaddle/Paddle
Dec 2021 - Nov 2022
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
ML Engineer
ML EngineerContributions:14 reviews, 8 commits, 14 PRs in 11 months
Contributions summary:Zuzanna contributed to the quantization of operations within the PaddlePaddle deep learning framework, focusing on the `slice`, `elementwise_add`, `elementwise_mul`, and `elementwise_sub` operators. They addressed formatting issues, implemented and refactored quantization logic for various operations, and modified existing code related to operator placement, scale calculation, and testing to support the newly quantized layers. This involved changes in C++ code related to the MKLDNN backend, enhancing the efficiency of the framework.
pytorchpythonparalleldeep-learningpaddlepaddle
zuzg/Paddle
Nov 2021 - Apr 2023
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Contributions:2 PRs, 84 pushes, 20 branches in 1 year 4 months
pytorchparalleldeep-learningreinforcement-learningindustrial
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