Vladislav Golubev
AI Frameworks Engineer at Intel Corporation
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolVladislav Golubev is an AI Frameworks Engineer based in Munich with seven years of experience building and optimizing inference tooling at Intel. He specializes in low-precision transformations and model quantization within the OpenVINO open-source toolkit, having contributed CPU-plugin kernel verification and extended the LPT framework for operations like ConvolutionBackpropData. Comfortable at the intersection of back-end engineering and ML, he focuses on performance-sensitive production code that tightens the gap between research models and efficient deployment. Holding a master's in computer science from UNN, he brings deep technical rigor and a knack for subtle, safety-first fixes (e.g., Klocwork-driven improvements) that keep large-scale inference software robust.
7 years of coding experience
2 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)
Github Skills (11)
convolution10
c-language10
cprogramming-language10
openvino10
optimization9
performance-optimization9
optmization9
optimisation9
back-end-development9
unit-testing8
cmake7
Programming languages (1)
Github contributions (5)
openvinotoolkit/openvino
Nov 2020 - Dec 2022
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1431 reviews, 115 commits, 318 PRs in 2 years 1 month
Contributions summary:Vladislav primarily worked on the OpenVINO toolkit, focusing on low-precision transformations. Their contributions involved fixing Klocwork issues and adding kernel verification to CPU-plugin tests for MatMul & Convolution. They also modified and extended the existing LPT (Low Precision Transformation) framework, involving the implementation of transformations related to specific operations like ConvolutionBackpropData. The user's work is focused on performance optimization through model quantization.
inference-enginepytorchmodel-optimizerdeep-learninggpu
v-Golubev/openvino
Jul 2020 - Apr 2025
OpenVINO™ Toolkit repository
Contributions:14 reviews, 4 PRs, 2032 pushes in 4 years 9 months
pytorchdeep-learninggpuopenvino-toolkitcomputer-vision
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