Oleg Vasilev
Systems Software Engineer at Neon
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolOleg Vasilev is a systems software engineer with 11 years of experience building virtualization, networking and storage infrastructure, currently developing a QEMU-based on-demand execution environment with live migration and autoscaling for PostgreSQL on Kubernetes. He has deep hands-on expertise with QEMU, libvirt and kernel subsystems from roles at Virtuozzo, Huawei and Intel, including rebasing hundreds of patches and enabling next-generation live-migration support upstream. His background spans production infrastructure at scale—load balancers and object storage at Yandex—to low-level performance work such as an ARM virtualization acceleration engine and PRIME GPU sharing. Oleg blends research and practical engineering: contributions to practical deep learning and reinforcement learning course repos show he also applies ML techniques and experiment-driven development. Based in Amsterdam, he combines systems-level rigor with a knack for shipping complex, cross-project integrations that make virtualization both faster and more operable.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Applied Mathematics and Informatics, Bachelor of Science - BS Applied Mathematics and Informatics at Higher School of EconomicsExchange Student Computer Science, Exchange Student Computer Science at University of HelsinkiStudent Computer Science, Student Computer Science at Yandex School of Data Analysis
Russian, English
Github Skills (15)
keras10
jupyter-notebook10
nlp10
deep-learning10
tensorflow10
python10
reinforcement-learning10
lasagne10
courses-resource9
machine-learning9
deeplearning-ai9
python-course9
data-preprocessing8
pytorch7
deep-reinforcement-learning7
Programming languages (16)
Github contributions (5)
yandexdataschool/Practical_RL
Feb 2017 - Feb 2018
A course in reinforcement learning in the wild
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:81 commits, 2 PRs, 84 pushes in 1 year
Contributions summary:Oleg implemented a crossentropy method for Atari Breakout and approximate Q-learning for the CartPole environment. They modified existing code for tabular CEM to plot distributions of rewards and tune the algorithm for positive average scores. Furthermore, they reworked a home assignment, involving convolutional agents for the DoomBasic environment.
pytorchgit-coursedeep-learningreinforcement-learningwild
yandexdataschool/Practical_DL
Sep 2016 - Nov 2017
DL course co-developed by YSDA, HSE and Skoltech
Role in this project:Data Scientist
Data ScientistContributions:39 commits, 32 pushes, 2 branches in 1 year 1 month
Contributions summary:Oleg contributed to the `practical_dl` repository, which focuses on deep learning courses. The commits show the addition of a Jupyter Notebook file, "Seminar-tf-main.ipynb," suggesting a focus on hands-on application. The notebook covers salary prediction using deep NLP methods, including data preprocessing, and convolutional neural networks, indicating a data science and machine learning focus. The user appears to be working on a practical deep learning project, possibly as part of a course.
deep-learningskoltechtheanohselasagne
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