Ivan Matantsev
Who Cares at Does it matter?
Bothell, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolIvan Matantsev is a Staff Software Engineer in Bothell, WA with 15+ years of experience building high-performance backends, ML infrastructure, and cloud services. He currently leads bi-quarterly releases of a cloud number-crunching platform that runs stochastic catastrophe models for insurers, owning REST APIs, data pipelines, performance tuning and mentoring. At Microsoft he helped productize research systems (Form Recognizer, FHIR server), contributed to Azure Redis and played a key role in open-sourcing dotnet/machinelearning—adding an in-memory data loader to streamline ML training. His work combines systems-level optimizations (e.g., scaling Bing multimedia measurement via SQL tuning and sharding) with pragmatic delivery: containers, SLA-focused performance tooling, and decoupled build infrastructure.
8 years of coding experience
17 years of employment as a software developer
Specialist, Applied math and computer science, 4.65, Specialist, Applied math and computer science, 4.65 at Vyatka State University of Humanities
Russian, English
Stackoverflow
11reputation
3kreached
2answers
0questions
Github Skills (15)
net10
machine-learning10
dotnet10
ml-dotnet10
mlnet10
csharp10
ml-net10
aspnet10
data-processing10
dotnet-core10
testing10
data-structure9
data-structures9
algorithms8
azure-form-recognizer6
Programming languages (7)
Github contributions (5)
dotnet/machinelearning
May 2018 - Apr 2019
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:136 commits, 305 PRs, 129 pushes in 11 months
Contributions summary:Ivan's contributions primarily focused on enhancing the data loading and processing capabilities within the ML.NET framework. They implemented an in-memory data loading mechanism, streamlining the training process by removing the need for file-based data sources. The user also added new test cases and refactored existing classes to improve code readability and maintainability. This work involved direct interaction with the data structures and processes used in ML pipelines.
machine-learning-platformdotnetml-netmachine-learningcsharp
Ivanidzo4ka/fhir-server
Aug 2020 - Jan 2022
A service that implements the FHIR standard
Contributions:410 pushes, 89 branches in 1 year 5 months
fhir-standardfhir
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