Sergey Naumets
Software Engineer at Google
Kirkland, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSergey Naumets is a software engineer with a decade of experience building full-stack internal web applications and backend services, currently improving employee badge systems at Google. He combines front-end expertise in Angular/Polymer and TypeScript with backend work in Java and occasional C++, and brings practical web accessibility know-how across major screen readers. Sergey’s open-source contributions to high-profile protein modeling projects (ColabFold and ProteinMPNN) show an appetite for domain-crossing work—he implemented UI controls for AlphaFold2 visualizations and demo pipelines that chain design models with structure prediction. Early roles at Qualcomm and AT&T highlight automation, scripting and web tooling that reduced manual effort and sped debugging cycles. A University of Washington computer engineering graduate, he pairs solid engineering fundamentals with a knack for turning complex scientific tools into usable, user-focused interfaces.
10 years of coding experience
1 year of employment as a software developer
Bachelor's Degree, Computer Engineering, 3.62/4.0, Bachelor's Degree, Computer Engineering, 3.62/4.0 at University of Washington
English, Russian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
uid10
pytorch10
jupyter-notebook10
machine-learning10
front-end-development10
ui-design10
python10
bioinformatics9
computational-biology9
jax8
Programming languages (7)
Github contributions (5)
sokrypton/ColabFold
Jul 2021 - Jan 2023
Making Protein folding accessible to all!
Role in this project:
Front-end Developer
Front-end DeveloperContributions:6 releases, 520 commits, 32 PRs in 1 year 6 months
Contributions summary:Sergey's commits show the creation and modification of an AlphaFold2 prediction structure interface implemented as an ipynb notebook. The changes suggest an effort to enhance the user experience by adding controls for visualization and options to customize the display of the protein structure, with changes focused on the 3dmol extension. The user is responsible for the design and implementation of UI elements.
accessibleproteinprotein-foldingfoldingbioinformatics
dauparas/ProteinMPNN
Jun 2022 - Sep 2022
Code for the ProteinMPNN paper
Role in this project:ML Engineer
ML EngineerContributions:16 commits, 1 PR, 15 pushes in 3 months
Contributions summary:Sergey's primary contribution revolves around a quick demo notebook for the ProteinMPNN project, which appears to be focused on protein design. The commits demonstrate the setup and utilization of a ProteinMPNN model, including loading weights, defining design options, and generating protein sequences. Further commits integrate AlphaFold2 for structure prediction and MMalign for TMscore calculations, expanding the analysis of designed protein structures.
deep-learningpytorch
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