Nick Geisler
Siri - Attention And Invocation
Kirkland, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNick Geisler is a machine learning-focused software engineer with 8 years of industry experience building ML-driven features for consumer platforms and devices. He has progressed through roles at Microsoft and Google and now contributes to Siri’s Attention and Invocation at Apple, blending product-facing ML with systems-level engineering. At Microsoft he worked as a Senior Program Manager integrating ML into UWP apps and contributed to the prominent Windows-Machine-Learning repo, adding ONNX model loading and MNIST samples to help developers ship models on Windows. His Google work on Pixel ML data and biometrics underscores hands-on experience with on-device ML, privacy-sensitive pipelines, and production deployment. A UBC-trained computer engineer based in Kirkland, WA, he brings a pragmatic mix of research-minded model integration and cross-team program delivery. Colleagues would note his knack for translating prototypical ML ideas into robust platform samples and SDK updates that accelerate developer adoption.
8 years of coding experience
13 years of employment as a software developer
Bachelor of Applied Science - BASc Computer Engineering, Bachelor of Applied Science - BASc Computer Engineering at UBC Electrical and Computer Engineering
Github Skills (12)
uwp10
machine-learning10
onnx10
csharp9
dotnet-core9
tensorflow9
neural-network9
deep-learning8
deeplearning-ai8
ai8
keras7
python6
Programming languages (4)
Github contributions (5)
microsoft/Windows-Machine-Learning
Mar 2018 - May 2021
Samples and Tools for Windows ML.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 23 commits, 12 pushes in 3 years 3 months
Contributions summary:Nick's contributions primarily center on integrating and developing machine learning models within a UWP (Universal Windows Platform) environment. Their work includes adding samples related to MNIST (Modified National Institute of Standards and Technology database) and implementing the loading and evaluation of ONNX (Open Neural Network Exchange) models. The commits demonstrate a focus on utilizing Windows ML to enable machine learning functionalities within the UWP applications. Further enhancements involve updating the SDK version.
pytorchcaffe2pythonwindowswinmltools
ngeisler11/windows-uwp
Mar 2018 - Mar 2018
Contributions:1 push in 1 day
xamldotnetuwpdevelopingwindows
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