Jaime Fernandez is a seasoned software engineer based in Zurich with 12 years of professional experience and a long technical career rooted in engineering roles at HP and currently at Google. He combines deep low-level algorithmic expertise with practical product delivery, having optimized core numerical routines in flagship scientific projects like NumPy and SciPy to achieve measurable performance and reliability gains. Jaime’s background spans manufacturing, imaging and printing algorithm R&D, firmware and pipeline engineering, and large-scale software development, reflecting a rare blend of hardware-aware systems thinking and high-performance back-end coding. Notably, his contributions to NumPy included type-specific binary searches and robust fixes to digitize, while his SciPy work introduced faster MINLIST-based filtering with O(n) average behavior and cleaned up memory and compiler issues. Comfortable in cross-disciplinary environments, he pairs MSc training in Industrial Engineering and Physics with hands-on problem solving across C/C++ and scientific computing stacks. Colleagues describe him as a pragmatic optimizer who finds elegant algorithmic improvements in mature codebases.
12 years of coding experience
19 years of employment as a software developer
MSc, Mathematics (unfinished), MSc, Mathematics (unfinished) at Universitat Autònoma de Barcelona
Master of Science (MSc), Physics, Master of Science (MSc), Physics at Universidad Nacional de Educación a Distancia - U.N.E.D.
MSc, Industrial (Mechanical) Engineering, MSc, Industrial (Mechanical) Engineering at Universidad Politécnica de Madrid
The fundamental package for scientific computing with Python.
Role in this project:
Back-end Developer
Contributions:261 commits, 199 PRs, 117 pushes in 5 years 1 month
Contributions summary:Jaime's primary contributions focused on optimizing the NumPy library's core functionalities. They implemented type-specific binary search functions for `searchsorted`, resulting in significant performance improvements. Furthermore, the user addressed and fixed bugs in the `digitize` function, including handling edge cases and improving code maintainability. Their work demonstrates a deep understanding of NumPy's internals and algorithms, as well as performance optimization techniques.
Contributions:66 commits, 44 PRs, 8 pushes in 4 years
Contributions summary:Jaime focused on optimizing numerical algorithms within the SciPy library. Their work involved improving the performance of the `min(max)imum_filter1d` function, enhancing its efficiency by changing the underlying computation algorithm to achieve an expected O(n) average performance. They also implemented a new algorithm, the MINLIST algorithm, for the same function, improving performance for ordered sequences. Furthermore, they addressed C90 compiler warnings and fixed memory leaks in related parts of the code.
scipypythonscientific-computing
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