Matthias Kramm is a seasoned software engineer with 17 years of experience, currently working at Google out of San Francisco. He blends deep systems and compiler expertise with practical backend engineering, contributing to high-impact open-source projects such as TensorFlow and Halide where he improved performance, Python bindings, and robustness. Previously Chief Scientist at Scribd and an academic researcher at Technical University Munich, he has a track record of turning research-grade ideas into production-ready systems. His contributions to mypy and typeshed show a commitment to Python typing and developer tooling, improving parsing, build processes, and library stubs. Comfortable across low-level compiler work and large distributed systems, he brings both rigorous academic training and long-term open-source stewardship—he even maintained the swftools project for over a decade.
Collection of library stubs for Python, with static types
Role in this project:
Back-end Developer
Contributions:198 commits, 347 PRs, 462 pushes in 3 years
Contributions summary:Matthias contributed to the `python/typeshed` repository, focusing on adding type stubs for Python 2 and 3 modules. Their work involved creating and modifying stub files to provide type hints, documenting function signatures, and organizing the modules alphabetically. The commits demonstrate the user's efforts to improve the type safety and maintainability of the project by providing detailed type annotations for Python libraries.
a language for fast, portable data-parallel computation
Role in this project:
Back-end Developer
Contributions:71 commits, 8 PRs, 49 comments in 10 months
Contributions summary:Matthias primarily contributed to the development of the Halide language, focusing on improving the Python extension. Their work involved fixing compiler warnings, adding features for Python extension generation, and improving the conversion of Python buffers. The user addressed bugs in float conversion and data type handling within the Python extension code, ensuring more robust and correct functionality. Furthermore, they added tests and documentation related to the Python bindings.
computationhexagonhalideparallelgpu
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