Walter Martin
Senior Software Engineer at Microsoft
Emeryville, California, United States
Summary
🤩
Rockstar🎓
Top SchoolWalter Martin is a Senior Software Engineer with six years of experience building production-grade ML and cloud systems at Microsoft, currently on the Azure Machine Learning team in Cambridge. He has driven features from ML interpretability (SHAP, LIME, EBM) to no-code transformer deployment and efficient, fungible GPU allocation for foundational models, blending C# service engineering with Python and occasional TypeScript. His contributions to the popular interpretml/interpret repo show practical expertise in explainable AI, including visualization and integration work for LIME, SHAP, PDP and EBM explanations. A Harvard CS graduate and former CS50/CS51 teaching fellow, he pairs rigorous technical training with clear communication and mentorship skills. Based in Emeryville, CA, Walter brings a pragmatic focus on reliability and operational APIs that make complex ML workflows accessible to product teams.
6 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Harvard UniversityBrunswick High School
English, Spanish, German
Github Skills (9)
scikit10
machine-learning10
explainable-artificial-intelligence10
python10
scikit-learn10
pandas8
numpy8
shap7
lime7
Programming languages (5)
Github contributions (5)
interpretml/interpret
Jul 2019 - Oct 2019
Fit interpretable models. Explain blackbox machine learning.
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 3 PRs, 14 comments in 3 months
Contributions summary:Walter primarily focused on enhancing the interpretability of machine learning models within the `interpret` repository. They implemented and refined visualization components, notably horizontal bar plots, to present model explanations. Their contributions included restructuring code for clarity, fixing sorting issues for diverse data representations, and incorporating MLI objects for explanations related to LIME, SHAP, PDP and EBM models, demonstrating proficiency in explainable AI techniques and associated libraries.
xaiinterpretmlfitinterpretableexplainability
wamartin-aml/mlflow
Dec 2020 - Sep 2023
Open source platform for the machine learning lifecycle
Contributions:70 pushes, 5 branches in 2 years 9 months
pythonlifecyclemlmachine-learningmlops
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