Zornitsa Kostadinova
Software Engineer at Preferred Networks, Inc.
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolZornitsa Kostadinova is a software engineer with 15 years’ experience blending C++ and Python expertise with machine learning, computer vision, and data visualization, currently driving engineering at Preferred Networks in Tokyo’s Chiyoda. Her background spans research and industry—from a Master’s thesis on structured forests at MPII to developing saliency-based video enhancement and production systems at scale—reflecting strong algorithmic and analytical foundations. She is fluent in CI/DevOps, containerization, testing, and agile practices, and brings hands-on experience shipping robust systems across the ML stack. An active open-source contributor, she has improved developer-facing documentation for prominent GPU-accelerated projects like CuPy and Chainer, helping make advanced GPU tooling more accessible. Colleagues value her for clarifying complex concepts in code and docs, a skill honed through years of teaching and technical writing alongside engineering.
15 years of coding experience
1 year of employment as a software developer
Mathematics and Natural Science profile, Mathematics and Natural Science profile at High School of Mathematics, VarnaBSc, Computer Science, BSc, Computer Science at Sofia University St. Kliment OhridskiMSc, Computer Science, Computer Science, MSc, Computer Science, Computer Science at Universität des Saarlandes
Bulgarian, English, German, Spanish, Japanese
Github Skills (7)
documentation10
python6
numpy5
cupy5
machine-learning4
deep-learning4
neural-network4
Programming languages (5)
Github contributions (5)
chainer/chainer
Jan 2016 - Apr 2017
A flexible framework of neural networks for deep learning
Role in this project:
Technical Writer
Technical WriterContributions:56 commits, 32 PRs, 21 pushes in 1 year 3 months
Contributions summary:Zornitsa primarily contributed to the repository by updating and improving the documentation. Their commits focused on refining tutorial content related to GPU usage, function explanations, and type checking. Furthermore, they corrected typos and formatting inconsistencies across multiple documentation files, including contribution guidelines and examples. This indicates a strong focus on enhancing the clarity and accuracy of the project's documentation.
cudapythonmxnetcaffe2flexible-framework
cupy/cupy
Jul 2017 - Aug 2017
NumPy & SciPy for GPU
Role in this project:Technical Writer
Technical WriterContributions:1 issue in 3 days
Contributions summary:Zornitsa primarily focused on updating and correcting documentation within the CuPy repository. Their commits addressed typos in code and documentation, updated tutorial sections on GPU usage, function implementations, and type checking, and added links to relevant examples in the code repository. The user's contributions improved the clarity, accuracy, and accessibility of the documentation for CuPy users.
cudapythoncusolvergpunumpy
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