Michael Sharp is a Senior Software Engineer with seven years of experience building high-performance, production-grade systems at Microsoft and now Neighbor, based in American Fork, Utah. He led ML.NET as the sole full-time developer, designing integrations like TorchSharp and managing third-party backends including Intel OneDAL, demonstrating both deep ML engineering and stakeholder ownership. His open-source contributions to the widely used dotnet/runtime and dotnet/machinelearning repos show a focus on numeric primitives, vectorized performance optimizations, and correctness-driven test automation. Comfortable across cloud migrations, CI/CD, and cross-platform runtime work, he combines low-level optimization skills with pragmatic system design. A detail-oriented engineer, he often pairs algorithmic improvements with rigorous testing to ensure correctness and performance in real-world workloads.
6 years of coding experience
12 years of employment as a software developer
Bachelor of Science (BS) Computer Science, Bachelor of Science (BS) Computer Science at Brigham Young University
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
Contributions:10 releases, 951 reviews, 104 commits in 3 years 8 months
Contributions summary:Michael's commits focus on fixing and improving the `sigmoid` function within the ML.NET framework for machine learning. They addressed a hardcoded value, implemented tests against LightGBM, and ensured correct functionality. The user also refactored the code to improve its formatting, comments, and parameter handling, showcasing expertise in machine learning trainer implementations.
.NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
Role in this project:
Back-end Developer & Test Automation Engineer
Contributions:265 reviews, 40 PRs, 21 pushes in 3 years 10 months
Contributions summary:Michael contributed significantly to the `.NET runtime` repository by adding new methods to the `TensorPrimitives` class and creating and modifying unit tests to ensure the correct behavior. These new methods include `CosineSimilarity`, `Distance`, `Dot`, `L2Normalize`, `SoftMax`, and `Sigmoid`. The user also focused on the performance of these methods by vectorizing `IndexOfMin/Max/Magnitude`, demonstrating a focus on optimization and testing.
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Michael Sharp - Senior Software Engineer at Neighbor