Jonathan Tripp
Senior Software Engineer at Roku
Cambridge, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolJonathan Tripp is a senior software engineer based in Cambridge with 11+ years of hands-on experience building everything from embedded firmware to large-scale C# and C++ systems. He combines a strong academic background—a PhD in Pure Mathematics—with practical expertise in functional languages (F#, Scala, Erlang) and rich web UIs, enabling rigorous algorithm design and clean, testable implementations. His work spans scientific, medical and archival domains, including contributions to Microsoft’s InnerEye medical imaging project and Project Silica for archival storage in glass. Comfortable across the stack, he has delivered mixed-integer optimisation engines, real-time instrument control, and cloud ML infrastructure on Azure. A habitual polyglot and mentor, he brings both low-level firmware insight (STM32/LPC NXP) and higher-level cloud/ML engineering to cross-disciplinary teams. Colleagues rely on him to translate complex mathematical requirements into production-grade, well-tested software.
11 years of coding experience
18 years of employment as a software developer
Part III Pure Mathematics, Part III Pure Mathematics at University of CambridgePhD Pure Mathematics, PhD Pure Mathematics at The University of SheffieldBSc Pure Mathematics, BSc Pure Mathematics at University of Birmingham
Github Skills (11)
medical-imaging10
custom-configuration10
configurations10
deep-learning10
system-configuration10
yml-configuration10
pytest10
python10
microsoft-azure9
azure9
dicom9
Programming languages (2)
Github contributions (5)
microsoft/InnerEye-DeepLearning
Dec 2020 - Nov 2021
Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:51 reviews, 14 commits, 26 PRs in 11 months
Contributions summary:Jonathan primarily contributed to the development and testing of a medical imaging deep learning library. Their work included refactoring and creating new configuration classes for segmentation models, specifically for head and neck and prostate applications. The user also added unit tests to validate the configurations, exclusion rules and summed probability rules. Furthermore, they modified the codebase to handle DICOM files and introduced a feature to zip DICOM series.
deep-learningmachine-learningazure-machine-learningdeep-learning-libraryimaging
microsoft/InnerEye-Gateway
Apr 2021 - Nov 2021
The InnerEye-Gateway is a Windows service that acts as a DICOM end point to run inference on https://github.com/microsoft/InnerEye-DeepLearning models.
Contributions:26 reviews, 12 commits, 32 PRs in 7 months
acts-aswindowsinferencewindows-servicedicom
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