Konstantin Khlebnikov
Associate
Amsterdam, North Holland, Russia
Summary
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RockstarKonstantin Khlebnikov is an experienced back-end engineer with 18 years of hands-on work in systems programming and automation, currently driving process automation at Deutsche Bank in Amsterdam. He specializes in performance, stability, and low-level infrastructure—contributions to high-profile projects like CatBoost and the Linux kernel show deep expertise in threading, I/O, memory management, and networking. Konstantin has tackled distributed database internals and reliability features in YDB, and built practical tooling such as ioping for disk I/O diagnostics. His open-source commits reveal a pragmatic focus on refactoring, benchmarking, and cross-platform robustness rather than headline features. Comfortable across C/C++ and system stacks, he combines production-minded engineering with a knack for eliminating subtle race conditions and resource leaks. Colleagues benefit from his steady, detail-oriented approach to making complex systems more efficient and dependable.
18 years of coding experience
Github Skills (47)
c-language10
system-programming10
command-line-interface10
machine-learning10
c1110
linux-kernel10
data-structure10
gradient-boosting10
c1710
disk10
overlayfs10
performance-optimization10
command-line10
data-structures10
cprogramming-language10
Programming languages (8)
Github contributions (5)
koct9i/ioping
May 2011 - Aug 2022
simple disk I/0 latency measuring tool
Role in this project:
Backend Developer
Backend DeveloperContributions:10 releases, 219 commits, 24 PRs in 11 years 4 months
Contributions summary:Konstantin primarily worked on implementing and improving the `ioping` tool, a utility for measuring disk I/O latency. Their commits show they developed the core functionality of the tool, including parsing options, measuring time, and displaying statistics. The contributions involved adapting the tool for different operating systems, adding features like rate limiting and JSON output, and improving overall performance and usability through code refactoring.
http3disklatencymeasuringfreebsd
catboost/catboost
Sep 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:Back-end Developer
Back-end DeveloperContributions:16 commits in 4 years 4 months
Contributions summary:Konstantin contributed to the CatBoost library by modifying core utilities and fixing issues. Their work involved optimizing string parsing and benchmarking, enhancing thread management, and addressing network-related problems specific to the OS X environment. They also removed obsolete network functionalities and added new features related to intrusive lists. The commits indicate a focus on performance improvements, system stability, and code refactoring within the core library.
kagglexgboostpythondata-mininglightgbm
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