Lincoln Waller
Principal Software Engineering Manager at Microsoft
Raleigh-Durham-Chapel Hill Area United States
Summary
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Rockstar🎓
Top SchoolLincoln Waller is a Principal Software Engineering Manager based in the Raleigh-Durham area with a deep background in low-level systems engineering, including user and kernel mode C/C++ development, system services, and systems architecture. He has progressed through multiple senior engineering roles at Microsoft since 2011, combining hands-on implementation with thorough code review and design stewardship. Lincoln has bridged systems and machine learning engineering by contributing notable eager-mode improvements to the high-profile ONNX Runtime project, adding operator support and tests to strengthen PyTorch integration. He excels at translating complex kernel- and service-level requirements into maintainable architectures and reliable implementations. Known for mentoring teams and raising engineering standards, he brings both detailed technical rigor and pragmatic leadership to large-scale platform efforts.
3 years of coding experience
12 years of employment as a software developer
Boston University
Github Skills (12)
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
onnx10
ai-framework9
neural-network8
hardware-acceleration8
python8
artificial-neural-networks8
cprogramming-language7
c-language7
Programming languages (1)
Github contributions (1)
microsoft/onnxruntime
Jul 2022 - Aug 2022
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Role in this project:
ML Engineer
ML EngineerContributions:66 reviews, 10 commits, 19 PRs in 28 days
Contributions summary:Lincoln made significant contributions to the ONNX Runtime project, focusing on eager mode improvements for PyTorch integration. They implemented and refined support for several ONNX operators, including masked_select, bitwise_and, and log_softmax, adding corresponding test cases. The user's work involved modifying the eager mode generator to improve operator support and ensure correct behavior within the ONNX Runtime framework.
runtimetrainingtensorflowai-frameworkaccelerator
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