Aleksandr Pivovar
Software Engineer, Machine Learning at Meta
United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolOleksandr Pyvovar is a Machine Learning Engineer with six years of experience building and optimizing scalable ML systems, currently working on large-scale recommendation models for Instagram Reels at Meta. He has a strong track record from Intel where he accelerated classical ML algorithms and researched novel neural network training frameworks, improving both performance and convergence. An active open-source contributor, he has optimized numerical algorithms and fixed key issues in prominent projects like oneDAL and scikit-learn-intelex, addressing memory constraints and distributed processing pain points. Oleksandr combines production-grade engineering with research instincts, focusing on model optimization, recommendation systems, and reliable integration into modern frameworks. Outside of work he channels his curiosity into game development, bringing a systems-oriented, product-minded perspective to user-facing experiences.
6 years of coding experience
5 years of employment as a software developer
Master's degree, Master's degree at Taurida 'V. I. Vernadskiy' National University, Simferopol
Stackoverflow
36reputation
104reached
1answer
0questions
Github Skills (15)
algorithm10
scikit10
scikit-learn10
pandas10
machine-learning10
machine-learning-algorithms10
c-language10
spark10
cpp10
cprogramming-language10
python10
data-analysis10
oneapi9
modin8
inteloneapi6
Programming languages (3)
Github contributions (5)
uxlfoundation/scikit-learn-intelex
Apr 2020 - Aug 2022
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 2 reviews, 7 commits in 2 years 4 months
Contributions summary:Aleksandr contributed to the integration of Modin for dataframe processing within the scikit-learn-intelex library, adding support and making conditional changes. They addressed a column name issue in the train_test_split function and also corrected a patching command. The user updated the library to enhance its usability.
pythonswrepoai-machine-learningdata-scienceintel
uxlfoundation/oneDAL
Oct 2019 - Jun 2021
oneAPI Data Analytics Library (oneDAL)
Role in this project:ML Engineer
ML EngineerContributions:34 reviews, 18 commits, 32 PRs in 1 year 7 months
Contributions summary:Aleksandr primarily focused on optimizing and configuring the KMeans spark sample code within the oneDAL library. They addressed memory constraints, making the KMeans implementation more efficient. Further contributions included modifications to SVD and other algorithms, demonstrating a focus on improving numerical algorithms for machine learning. The user also resolved bugs in decision forest and GBT implementations.
swrepodata-analyticsanalyticscppdata-analysis
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