Alejandro Munoz
Senior Software Engineer AI at Meta
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolAlejandro Munoz is a Senior Software Engineer AI with two decades in industry and over a decade in Silicon Valley, currently advancing fairness and responsible AI at Meta. He brings deep expertise in testing, developer experience, and scaling infrastructure from roles at Microsoft, Google, Intuit and startups, with a track record of shortening build times and automating culprit bisection for faster releases. Alejandro contributes to open-source ML tooling—improving PyTorch's Captum tests and usability—which complements his hands-on work on mitigation scaling for production performance and capacity. Trained in electrical engineering and computer science at ITESM, he blends rigorous academic grounding with pragmatic engineering. Colleagues rely on him to tame flaky tests and brittle releases, while he pursues creative outlets that mirror his problem-solving drive. Based in Austin, he balances deep infra skills with growing machine learning expertise from Meta’s RAISE program.
13 years of coding experience
11 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Instituto Tecnológico y de Estudios Superiores de MonterreyBachelor of Science (BS), Electrical and Electronics Engineering, Bachelor of Science (BS), Electrical and Electronics Engineering at Instituto Tecnológico y de Estudios Superiores de Monterrey / ITESM
Spanish, English, French
Github Skills (5)
pytorch10
interpretation10
unit-test10
python10
machine-learning9
Programming languages (2)
Github contributions (5)
pytorch/captum
Nov 2022 - Nov 2022
Model interpretability and understanding for PyTorch
Role in this project:
ML Engineer & QA Engineer
ML Engineer & QA EngineerContributions:2 reviews, 4 commits, 11 PRs in 16 days
Contributions summary:Alejandro primarily contributed to improving the usability and functionality of the `captum` library, which is focused on model interpretability in PyTorch. Their contributions included adding support for nested progress bars, fixing mypy errors, and resolving broken unit tests. They also addressed time consumption issues in tests related to the `kernel_shap` module, and modified existing tests.
pytorchinterpretable-aifeature-importanceunderstandinginterpretability
cyrjano/AuQuery
Feb 2013 - Jan 2014
Contributions:30 commits in 11 months
browserjqueryjavascriptjquery-likeautomation
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