Senior Machine Learning Engineer at Carbon Engineering
Vancouver, British Columbia, Canada
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Summary
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Amin Sadeghi is a Senior Machine Learning Engineer with over a decade of experience applying mathematical and scientific computing techniques to real-world engineering problems, currently working on scientific ML and surrogate modeling in Vancouver. He holds a PhD in Chemical Engineering and has built expertise across PDEs, reduced-order models, CFD, and high-performance computing from research roles at McGill, Waterloo, and U of T. Amin combines research rigor with production-focused software skills—contributing to open-source projects like Numba (test/QA for typed.List) and OpenPNM—improving robustness and core library features. He has a strong track record in interdisciplinary collaboration, supervising research and translating complex models into deployable systems for energy and resource applications. Known for effective communication and published research, he often bridges theory and practice by embedding probabilistic and Bayesian methods into engineering workflows. His background in both optimization and time-series/active learning gives him a rare blend of modeling depth and pragmatic systems engineering.
10 years of coding experience
11 years of employment as a software developer
Master of Science - MS, Chemical Engineering, Master of Science - MS, Chemical Engineering at Sharif University of Technology
Doctor of Philosophy - PhD, Chemical Engineering, Doctor of Philosophy - PhD, Chemical Engineering at McGill University
A Python package for performing pore network modeling of porous media
Role in this project:
Backend Developer
Contributions:22 reviews, 1909 commits, 274 PRs in 6 years 11 months
Contributions summary:Amin primarily focused on implementing features for the OpenPNM core library. This includes fixing a bug that raised an exception, adding a mechanism to return the version number, refactoring a code block, and adding a validation check for data health. The user also worked on the internal aspects of the algorithm as well as on other data types.
Contributions:7 reviews, 19 commits, 1 PR in 26 days
Contributions summary:Amin primarily contributed to the quality assurance of the Numba project by writing and refactoring unit tests. They focused on testing the `typed.List` data structure, specifically its string representation in IPython environments. The user implemented tests to cover various scenarios, including long lists and the behavior of `repr()` in different contexts, as well as fixing the identified issues. This work enhanced the robustness and reliability of Numba's features.
cudapythonparallelnumpynumba
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Amin Sadeghi - Senior Machine Learning Engineer at Carbon Engineering