Krzysztof Chalupka
Quantitative Developer
United States
Summary
👤
Senior🎓
Top SchoolKrzysztof Chalupka is a quantitative developer with 11 years of experience translating computer vision and deep learning research into production-quality systems across startups and large tech companies. He has driven applied CV and ML features at Meta Reality Labs and AWS SageMaker Ground Truth, including leading the creation of auto-labeling for large-scale annotation pipelines. His background spans research (Caltech PhD and postdoc) and industry R&D at Siemens and Max Planck, giving him deep expertise in causal inference, probabilistic modeling, and 3D/mesh processing. An active contributor to Facebook Research’s PyTorch3D, he implemented robust mesh submeshing, texture handling, and testing utilities that improve rendering and reproducibility. Now at Jump Trading, he combines low-latency quantitative systems thinking with a strong ML research pedigree to build practical, high-impact products. Colleagues describe him as someone who bridges rigorous academic methods with pragmatic engineering to ship reliable, production-ready ML features.
11 years of coding experience
9 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at The University of EdinburghCalifornia Institute of Technology
Polish, Chinese
Github Skills (12)
3d10
pytorch10
geometry10
geom10
mesh-processing10
testing9
computer-graphics9
c-language8
cuda8
opengl8
cprogramming-language8
machine-learning6
Programming languages (4)
Github contributions (5)
facebookresearch/pytorch3d
Apr 2022 - Aug 2022
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:30 commits, 1 PR, 15 comments in 4 months
Contributions summary:Krzysztof's contributions focused on enhancing the `pytorch3d` library with new features, particularly related to mesh processing and rendering capabilities. They implemented submeshing functionality, including the ability to split meshes into submeshes and handle textures within submeshes. Furthermore, the user added mesh equality checks and a sorting function (`to_sorted`) for vertices, which are crucial for testing and consistent mesh representation. The user also modified existing classes and functions within the library.
3ddeep-learningpytorch3d
kjchalup/neural_networks
May 2017 - Jul 2017
Contributions:81 commits, 63 pushes, 1 branch in 2 months
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