Ivan Matantsev
Who Cares at Big corporation
Bothell, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolIvan Matantsev is a pragmatic senior software engineer with eight years of experience building high-throughput cloud systems and a longer history in .NET development. He has shipped production-grade services at Microsoft and RMS, working on high-performance FHIR servers, Azure Redis offerings, and exabyte-scale catastrophe loss calculations that stitch modeling, finance, and UI teams together. Comfortable across the stack, he combines hands-on backend and ML engineering—contributing to ML.NET by implementing in-memory data loaders and improving pipeline testability—with operational ownership of logging, CI/CD and SLA-driven infrastructure. Based in Bothell, WA, he excels at turning complex domain models into reliable, monitored services and is known for moving quickly from prototypes to customer-facing releases. Notably, he blends deep legacy .NET experience with modern cloud-native practices to solve large-scale data and modeling problems.
8 years of coding experience
17 years of employment as a software developer
Specialist, Applied math and computer science, Specialist, Applied math and computer science at Vyatka State University of Humanities
Russian, English
Stackoverflow
11reputation
3kreached
2answers
0questions
Github Skills (14)
net10
asp-net10
machine-learning10
dotnet10
mlnet10
csharp10
mldotnet10
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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