Peter Goldsborough
Co-Founder And CTO at Rune Technologies
Washington, District of Columbia, United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Goldsborough is a seasoned technical founder and CTO with 12 years of experience building low-level, performance-sensitive infrastructure and ML systems across startups and defense-focused engineering at Anduril and Rune Technologies. He combines deep C++/CUDA expertise—evidenced by contributions to PyTorch core extensions and audio/LLTM implementations—with a track record of shipping large-scale distributed systems at Facebook and instrumenting real-time data and compiler tooling. Equally comfortable leading teams and writing core libraries, he’s delivered ML & data infrastructure, optimized neural network primitives, and modernized legacy C code into robust C++ extensions. Active in open source (scikit-image, PyTorch tutorials) and hands-on with quantization and compiler improvements, he brings both research-grade rigor and production-first pragmatism. Based in Washington, D.C., he also volunteers cyber expertise to the Marine Corps Cyber Auxiliary, blending national-security priorities with product-driven logistics innovation.
12 years of coding experience
10 years of employment as a software developer
Bachelor of Science (B.Sc.) Computer Science, Bachelor of Science (B.Sc.) Computer Science at Technical University of MunichBG/BRG St.Martin Villach
German, English, French, nerd
Stackoverflow
1,388reputation
181kreached
22answers
12questions
tensorflow
top-5%
Github Skills (49)
torchscript10
unit-testing10
code-optimization10
pytorch10
c-language10
caffe10
pytest10
operation10
python10
image-processing10
testing10
tensorrt10
machine-learning10
audio-processing10
scikit-image10
Programming languages (15)
Github contributions (5)
pytorch/extension-cpp
Mar 2018 - Aug 2018
C++ extensions in PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 1 PR, 13 pushes in 5 months
Contributions summary:Peter contributed to the PyTorch C++ extension repository by fixing CUDA kernels to support batch sizes greater than one. They added and refined checks for forward and backward passes, ensuring the correctness of the LLTM implementation. Furthermore, they updated the codebase by making CUDA tensors contiguous and correcting the bias dimension. These changes demonstrate a focus on improving the performance and accuracy of the CUDA-based LLTM implementation within the PyTorch ecosystem.
pytorchcudacpplibtorchextensions
scikit-image/scikit-image
Aug 2017 - Nov 2017
Image processing in Python
Role in this project:Data Scientist
Data ScientistContributions:20 commits, 1 PR, 18 comments in 2 months
Contributions summary:Peter primarily contributed to the development of a `generate_shapes` module within the scikit-image library. Their work involved the implementation of functions to create various geometric shapes (rectangles, circles, and triangles) with random properties, including size and color. These contributions included API redesign, fixing bugs related to integer division in Python 2, and the addition of unit tests, demonstrating a focus on both the functionality and quality of the image generation capabilities. The user also refactored the code to improve efficiency and code readability.
image-processingpythoncomputer-visionimage
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