Alexey Bukhtiyarov
Member Of Technical Staff at Slingshot AI
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolAlexey Bukhtiyarov is a Member of Technical Staff based in London with nine years of software engineering experience spanning startups and major tech firms. He has strong ML and distributed-systems chops demonstrated by contributions to the decentralized deep learning project Hivemind (adding collaborative optimizers, training averagers and Python 3.7 compatibility), and a track record of shipping core functionality rather than superficial fixes. His career includes engineering roles at Yandex and Kaspersky and leadership at Ex-human before joining Slingshot AI, reflecting a mix of hands-on development and team-leading experience. Educated at MIPT and the Yandex School of Data Analysis, he combines rigorous academic grounding with practical expertise in collaborative training systems and production-ready ML infrastructure.
9 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Applied Mathematics and Computer Science, Bachelor's degree, Applied Mathematics and Computer Science at NRU MPEIMaster's degree, Computer Science, Master's degree, Computer Science at Moscow Institute of Physics and Technology (State University) (MIPT)Yandex School of Data Analysis
English, Russian
Github Skills (11)
pytorch10
machine-learning10
distributed-systems10
distributed-training10
deep-learning10
python10
asyncio8
artificial-neural-networks7
neural-network7
dockers5
docker5
Programming languages (2)
Github contributions (5)
learning-at-home/hivemind
Feb 2021 - Nov 2021
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 8 commits, 16 PRs in 9 months
Contributions summary:Alexey primarily contributed to the development and refinement of the Hivemind project, a decentralized deep learning framework. They implemented critical features such as a copytree implementation for Python 3.7 compatibility and serialized DHTID sources with msgpack, demonstrating a focus on improving core functionality. Further contributions include fixing scalar deserialization, adding collaborative optimizer and training averager, and creating a collaborative ALBERT training example, which showcases the user's ability to contribute to training frameworks.
pytorchhiveminddhtasynciovolunteers
leshanbog/PreSumm
Apr 2020 - Nov 2020
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders
Contributions:55 commits, 55 pushes, 1 branch in 7 months
nlptext-summarizationencoderssummarizationnatural-language-processing
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