Sergey Kolychev
Distinguished Engineer at WhiteHat Security
Portland, Oregon, United States
Summary
🤩
Rockstar🎓
Top SchoolSergey Kolychev is a Distinguished Engineer based in Portland with over 20 years of systems and back-end engineering experience and a decade of recent hands-on work building production ML systems. He architected and ported a Perl interface to the MXNet deep-learning framework and introduced ML pipelines, metrics, and inference microservices into a large-scale dynamic web-scanning product, later migrating models to PyTorch and AzureML. Sergey’s core strengths are low-level, high-performance engineering—OO Perl, C/C++, Unix—and bridging that world to modern ML tooling (MXNet, transformers, Hugging Face) and cloud deployment. He has a track record of turning complex scanning and classification problems into automated, metrics-driven pipelines that reduced noise and operational load. Fluent in pragmatic cross-language integration, he also brings a surprising kombination of production-grade web crawling/regex expertise and formal engineering training in mechanical/nuclear engineering.
9 years of coding experience
20 years of employment as a software developer
Bachelor, Language Arts (Japanese language), Bachelor, Language Arts (Japanese language) at Foothill CollegeMaster of Engineering in Mechanical Engineering, Nuclear Stations, Master of Engineering in Mechanical Engineering, Nuclear Stations at Bauman Moscow State Technical University
Japanese, Russian
Github Skills (7)
mxnet10
rnn-model10
deep-learning10
perl10
n10
apidoc9
api9
Programming languages (4)
Github contributions (5)
apache/mxnet
Feb 2017 - Feb 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 4 reviews, 31 commits in 3 years
Contributions summary:Sergey primarily contributed to the development of the Perl5 interface for the MXNet deep learning framework. Their work involved the initial implementation of the Perl interface, addressing code review feedback to refine and expand functionality. The user added new features, fixed bugs and improved the interface with focus on stability, and compatibility with other systems, and made improvements to the RNN layers. They also synced the Perl package to the current state of the Python interface, updating the RNN API, and initializing constructors.
pythonschedulerdataflowmutationdata-science
sergeykolychev/perl-nats-streaming
Nov 2017 - Jul 2018
Contributions:1 release, 8 commits, 18 pushes in 8 months
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