Michał Dobrzański
Senior Software Engineer at Sliide
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolMichał Dobrzański is a Senior Software Engineer based in Warsaw with 10 years of experience building cloud-integrated mobile applications and cross-platform solutions. He specializes in Android and Kotlin (including Kotlin Multiplatform), Java, Python and C++, and has driven architecture, caching and automation efforts across e-commerce, healthcare, and embedded/IoT domains. His background in data science and hands-on work implementing GPU-accelerated deep learning examples demonstrates a rare blend of mobile engineering and ML experimentation. At Sliide and previous roles he’s delivered system-integrated native apps with custom OS capabilities, native bridges and JNI/C++ components, while also authoring Python tools and mobile automation. Colleagues rely on him for pragmatic architecture, performance tuning and bridging low-level platform nuances with high-level product needs.
10 years of coding experience
6 years of employment as a software developer
Master of Science (M.S.), Information Technology, A, Master of Science (M.S.), Information Technology, A at Warsaw University of Technology
English, German, Polish
Stackoverflow
1,773reputation
1.4mreached
50answers
10questions
flutter
top-5%
Github Skills (18)
python10
machine-learning10
deep-learning10
neural-network10
cuda10
theano10
regularization9
flutter9
early-stopping8
convolutional-neural-networks8
dart6
firebase6
cocoapods6
android-studio6
android6
Programming languages (10)
Github contributions (5)
MichalDanielDobrzanski/DeepLearningPython
Jul 2016 - Feb 2021
neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 7 PRs, 12 pushes in 4 years 7 months
Contributions summary:Michał primarily contributed to the implementation and testing of neural network models within the context of a deep learning project. They introduced functionalities like early stopping and vanishing gradient problem analysis. They also demonstrated proficiency in adapting code examples for use with Theano and CUDA, which highlights a focus on optimizing model performance and leveraging GPU acceleration.
cudapythonintegratedgputheano
MichalDanielDobrzanski/EpidemyGraphs
Oct 2016 - May 2017
Contributions:18 commits, 3 pushes, 1 branch in 6 months
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