Ignatiy Kolesnichenko
Middle-Level TEAM&TECH Lead At Yandex
Moscow, Moscow City, Russia
Summary
🤩
Rockstar🎓
Top SchoolIgnatiy Kolesnichenko is a seasoned software engineer and team/tech lead with 15 years at Yandex, driving scalable distributed systems, schedulers and API ecosystems while growing and managing multiple engineering teams. He combines deep hands-on expertise in scheduler algorithms, resource management and performance optimizations (including a 5x scheduler upscale and >5% cluster compute savings) with practical DevOps and build-system modernization across large codebases. He helped open-source YTsaurus, led migration to unified build systems, and oversaw GPU cluster installations and benchmarking for production workloads. As a former CTO and cofounder in an NGS startup and an academic lecturer, he brings both product-oriented leadership and rigorous algorithmic training in mathematical logic. Less obvious: he routinely blends low-level C++/Python integration work with high-level architecture and hiring/interview process improvements to scale teams and systems.
15 years of coding experience
10 years of employment as a software developer
Lomonosov Moscow State University
Github Skills (12)
gradient-boosting10
testing9
python9
c-language8
data-mining8
pytest8
cprogramming-language8
data-science7
decision-tree7
machine-learning7
gpu6
git5
Programming languages (7)
Github contributions (5)
catboost/catboost
Mar 2018 - Jan 2023
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Role in this project:
Backend & DevOps Engineer
Backend & DevOps EngineerContributions:62 commits in 4 years 10 months
Contributions summary:Ignatiy primarily focused on improving the logging and testing infrastructure within the CatBoost repository. They made adjustments to the test call logging system, enhancing its clarity. Additionally, they contributed by moving files related to the FarmHash library and addressed issues related to core file handling. Their contributions suggest involvement in code maintenance, build process improvements, and potential performance optimizations.
kagglexgboostpythondata-mininglightgbm
Kontakter/Ignat
Apr 2015 - Mar 2024
Contributions:19 pushes in 9 years
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