Dmitrii Murygin, Developer in Bishkek, Chuy Province, Kyrgyzstan
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Dmitrii Murygin

Verified Expert  in Engineering

Software Developer

Bishkek, Chuy Province, Kyrgyzstan
Toptal Member Since
December 7, 2021

Dmitrii is an experienced C++ developer and deep learning engineer. As a middle C++ developer he has five years of experience in research computation related projects. As a deep learning engineer he has two years of solid experience in NLP direction connected to transformer and BERT based models.


C++, CMake, Clang, Google Test, Git, Ubuntu, Visual Studio Code (VS Code)...
Huawei Technologies Co.
Python 3, TensorFlow, PyTorch, BERT, Custom BERT, Neural Networks




Preferred Environment

Ubuntu, Visual Studio Code (VS Code), CMake, Clang, Python 3, Vim Text Editor, C++

The most amazing...

...model I’ve developed is a BERT model in C++ that has the same performance on a mobile device as made using ONNX Runtime.

Work Experience

Research Software Engineer

2021 - PRESENT
  • Developed a new method of 3D image segmentation based on supervoxel clusterization.
  • Developed a new method of pore-network extraction based on discrete Morse theory.
  • Implemented three parallel algorithms for 3D image segmentation and two parallel algorithms for 3D image filtering.
  • Implemented a cutting-edge programming module to simulate fluid filtration in pore-network models.
Technologies: C++, CMake, Clang, Google Test, Git, Ubuntu, Visual Studio Code (VS Code), OpenMP


2018 - 2021
Huawei Technologies Co.
  • Developed a BERT model for mobile devices using C++. Compared it with other BERT versions converted from the most famous frameworks.
  • Made some contributions to various projects such as TensorFlow, Swift, LLVM, Stardust, and TVM.
  • Implemented the following neural network compression techniques: quantization, distillation, and prunning.
Technologies: Python 3, TensorFlow, PyTorch, BERT, Custom BERT, Neural Networks

Mobile BERT Benchmark
A benchmark for BERT I created that is converted by ONNX for mobile devices. It is possible to get performance for tiny, base, and large versions of the BERT model. To create a mobile BERT version, it uses the ONNX Runtime tool and creates a fused BERT model.
2014 - 2018

Bachelor's Degree in Mathematics and Computer Science

Moscow State University - Moscow, Russia


TensorFlow, OpenMP, PyTorch


CMake, Git, Vim Text Editor, Open Neural Network Exchange (ONNX)


Ubuntu, Visual Studio Code (VS Code)


C, C++, Python 3


Google Test


Clang, Deep Learning, LLVM, Machine Learning, Natural Language Processing (NLP), BERT, Custom BERT, Neural Networks, Generative Pre-trained Transformers (GPT)

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