Valeriy Klyukin
Machine Learning Engineer at Reddit, Inc.
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolValeriy Klyukin is a Machine Learning Engineer based in Amsterdam with eight years of industry experience building and productionizing deep learning and NLP systems. He has driven cost and latency optimizations at scale—migrating training infrastructure to AWS to cut expenses, architecting Cassandra-based cache migrations, and achieving dramatic latency reductions in entity extraction at Yandex. His work spans large-scale language models (contributing to YaLM), embedded keyword spotting, and practical tooling improvements in open-source projects like CatBoost where he enhanced cross-validation, model saving, and test coverage. Valeriy blends research rigor (Normalizing Flows and knowledge distillation at CERN/LAMBDA) with hands-on engineering, and he founded an internal deep learning reading group to engage 90+ ML practitioners. He routinely bridges prototype to production, optimizing both model quality and operational efficiency across cloud and edge environments.
8 years of coding experience
4 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at State University — Higher School of EconomicsML Engineer, ML Engineer at Yandex School of Data Analysis
English, Russian
Github Skills (8)
decision-tree10
machine-learning10
python10
gradient-boosting10
testing10
cprogramming-language5
c-language5
r4
Programming languages (4)
Github contributions (5)
catboost/catboost
Mar 2021 - Apr 2021
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:
ML Engineer
ML EngineerContributions:13 commits, 1 PR, 1 comment in 1 month
Contributions summary:Valeriy primarily contributed to the testing and enhancement of the CatBoost library. Their work focused on adding tests for cross-validation functionality, including return models and related methods. They also addressed code quality issues through fixing flake errors and description updates. Furthermore, the user made changes to the core cross-validation and model saving mechanisms.
kagglexgboostpythondata-mininglightgbm
vklyukin/nf_distillation
Dec 2020 - Aug 2021
Knowledge Distillation of Normalizing Flows
Contributions:98 commits, 54 PRs, 58 pushes in 7 months
pytorchdistillationknowledge-distillationflowsknowledge
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