Evgeny Fomin
Software Engineer at BridgeApp
Belgrade, Central Serbia
Summary
👤
Senior🎓
Top SchoolEvgeny Fomin is a versatile software engineer with 11 years of professional experience building backend systems, automation, and ML-enabled applications across Rust, C++, Python and embedded C. He has driven production Rust work at fintech and healthcare companies, contributed performance-minded improvements to the widely used dlib C++ ML toolkit, and has deep experience integrating third‑party APIs, gRPC/GraphQL, and CUDA workloads. Comfortable moving between low-level optimizations (SIMD/FHoG changes, SSE2 support) and higher-level system design, he focuses on reliable, maintainable solutions that reduce operational routine for end users. Based in Belgrade, he brings a pragmatic blend of research-orientation from ITMO and hands-on engineering across startups and outsourcing teams. Notably, he’s been coding since 1992, giving him rare long-term perspective on evolving languages and tooling.
11 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree, Computer Science and Engineering, Bachelor’s Degree, Computer Science and Engineering at ITMO University
Русский, English
Stackoverflow
2,501reputation
137kreached
69answers
10questions
Github Skills (15)
computer-vision10
machine-learning10
deeplearning-ai10
deep-learning10
cpp10
cplus10
python9
dlib6
face-detection6
rust6
opencv6
qt6
makefile6
android6
image-processing6
Programming languages (5)
Github contributions (5)
davisking/dlib
Mar 2016 - Aug 2017
A toolkit for making real world machine learning and data analysis applications in C++
Role in this project:
ML Engineer
ML EngineerContributions:50 commits, 29 PRs, 177 comments in 1 year 5 months
Contributions summary:Evgeny made several contributions focused on the development and improvement of the `dlib` machine learning toolkit. Their commits added options to the simple object detector trainer, including an `upsample_limit` parameter, and implemented automatic support for SSE2 on x64 MSVC builds, which enhanced performance. Furthermore, they modified the FHoG calculations to use floats rather than doubles, and then added functionality to leverage SIMD8 for FHOG feature computations.
data-analysispythondata-sciencedeep-learningc-plus-plus
e-fominov/dlib
May 2017 - Dec 2017
A toolkit for making real world machine learning and data analysis applications in C++
Contributions:35 commits, 2 PRs, 130 pushes in 7 months
data-analysismachine-learningdata-science
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.