Vadym Doroshenko
Senior Software Engineer at Google
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolVadym Doroshenko is a Senior Software Engineer based in Munich with six years of focused experience building privacy-preserving data infrastructure at Google and earlier work across Chrome, Samsung, and research roles. He brings deep technical rigor from a PhD in algebra and number theory to practical systems work in differential privacy, data anonymization, computer vision, and stochastic modeling. Vadym has contributed to prominent open-source projects such as TensorFlow Privacy and OpenMined/PyDP, improving membership inference attacks and stabilizing core differential privacy libraries by fixing algorithms, tests, and serialization. Colleagues rely on him for thoughtful refactors that bridge theory and production — for example, correcting loss calculations and enabling probability-based attacks in TF Privacy. He combines academic discipline with hands-on backend and ML engineering, making complex privacy guarantees usable in real systems. Fluent in both research and large-scale product codebases, he thrives on tightening the gap between sound math and reliable software.
7 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Algebra and Number Theory, Doctor of Philosophy (Ph.D.), Algebra and Number Theory at Kyiv National Taras Shevchenko UniversityMaster, Mathematics, Master, Mathematics at Taras Shevchenko National University of Kyiv
Github Skills (17)
python10
machine-learning10
data-structure10
privacy10
tensorflow10
data-structures10
testing9
c-language9
algorithm9
algorithms9
numpy9
cprogramming-language9
cpp8
deserialization8
serialization8
Programming languages (3)
Github contributions (5)
OpenMined/PyDP
Oct 2021 - Nov 2022
The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 13 reviews, 18 commits in 1 year
Contributions summary:Vadym primarily contributed to fixing and cleaning up the code base, including removing unused code and formatting the code. They made updates to the Google C++ Differential Privacy library and fixed related compilation errors. Their work involved modifications to various files, including binding, algorithms, and test files, which suggests involvement in core library functionality and testing. They also added serialization and deserialization capabilities for algorithm summaries.
differential-privacypythonpython-wrappercpp
tensorflow/privacy
Sep 2020 - Jun 2021
Library for training machine learning models with privacy for training data
Role in this project:ML Engineer
ML EngineerContributions:12 reviews, 7 commits, 13 comments in 9 months
Contributions summary:Vadym primarily focused on improving the membership inference attack capabilities within the TensorFlow Privacy library. They updated and refactored existing attack implementations, including adding the ability to use probabilities within the attack. The contributions involved modifying the data structures to handle probabilities, and updating the related unit tests. Moreover, the user fixed the calculation of loss, demonstrating a strong understanding of the underlying machine learning concepts.
machine-learning-modelstraining-datamachine-learningprivacy
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