Paulina Gacek
Research And Teaching Assistant at Google
Krakow, Lesser Poland Voivodeship
Summary
🤩
Rockstar🎓
Top SchoolPaulina Gacek is a software engineer and researcher with five years of experience bridging ML systems and production engineering, currently working on Isolated Web Apps at Google while pursuing an MS in Artificial Intelligence at AGH University of Krakow. She has contributed to high-performance deep learning infrastructure—optimizing operators and migrating code for the PaddlePaddle framework to leverage OneDNN—demonstrating deep backend and ML-engineering chops. Her internships span Google (SRE and software engineering), Intel (deep learning software), and Nokia, giving her practical experience across reliability, performance, and low-level C++ work. At AGH she balances research and teaching with hands-on team leadership from her time managing AGH Racing projects. Colleagues describe her as someone who comfortably moves between research prototypes and production-grade optimizations, with a knack for squeezing extra performance out of ML pipelines. Based in Kraków, she brings a pragmatic, systems-first approach to building scalable ML-enabled software.
5 years of coding experience
3 years of employment as a software developer
Master of Science - MS Artificial Intelligence, Master of Science - MS Artificial Intelligence at AGH University of Krakow
English, German, Polish
Github Skills (12)
paddlepaddle10
machine-learning10
deeplearning-ai10
deep-learning10
edn10
python10
back-end-development10
efficientnet10
cprogramming-language9
neural-network9
c-language9
distributed-training8
Programming languages (4)
Github contributions (5)
PaddlePaddle/Paddle
Sep 2022 - Jan 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）
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:42 reviews, 9 commits, 24 PRs in 3 months
Contributions summary:Paulina primarily focused on migrating and optimizing operators within the PaddlePaddle framework for the OneDNN backend. They migrated several operators (Shape, Sum, Transpose2) to the phi library, improving the framework's performance and efficiency. Their work included refactoring code, correcting compilation errors, and incorporating OneDNN optimizations to accelerate deep learning computations. This indicates a strong understanding of both back-end development and machine learning frameworks.
pytorchpythonparalleldeep-learningpaddlepaddle
PaulinaGacek/Paddle
Sep 2022 - 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:120 pushes, 21 branches in 7 months
pytorchparalleldeep-learningreinforcement-learningindustrial
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