Mike Mccarty
Senior Engineering Manager, RAPIDS at NVIDIA
Leesburg, Virginia, United States
Summary
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Rockstar🎓
Top SchoolMike Mccarty is a senior engineering manager at NVIDIA leading build engineering for RAPIDS with nine years of focused experience in data science and machine learning infrastructure. He has progressed from hands-on HPC and platform engineering to product and people leadership, shepherding projects like Legate, cuNumeric and distributed computing integrations with Dask across cloud, HPC, and scientific stacks. Known for scaling Python-based ML workflows in large enterprises (Capital One) and contributing to PyData open-source ecosystems, he blends product strategy with deep technical fluency in Python, NumPy, Pandas and CUDA. Mike’s background includes platform, DevOps and full-stack roles that inform pragmatic, production-ready tooling and developer experiences. He also advises non-profits and open-source organizations, signaling a commitment to community-driven software and reproducible science. Based in Leesburg, VA, he pairs academic credentials from UIUC and Stanford LEAD with a track record of moving research-grade code into scalable, maintainable systems.
10 years of coding experience
18 years of employment as a software developer
Stanford LEAD Professional Certificate, Stanford LEAD Professional Certificate at Stanford University Graduate School of BusinessB.S. Physics, B.S. Physics at Morehead State UniversityComputer Science, Computer Science at University of Illinois Urbana-Champaign
Stackoverflow
21reputation
1kreached
2answers
0questions
Github Skills (139)
similarity-search10
gbm10
xgboost10
dataflow10
python10
nearest-neighbors10
parallel-computing10
gpu10
intake10
pydata10
cpp10
clustering10
pandas10
statistics10
machine-learning-algorithms10
Programming languages (14)
Github contributions (5)
mmccarty/notebooks
Aug 2017 - Aug 2022
Contributions:8 pushes, 1 branch in 5 years 1 month
mmccarty/dask
May 2019 - Aug 2022
Parallel computing with task scheduling
Contributions:13 pushes, 5 branches in 3 years 3 months
schedulingparalleltask-schedulingcomputingparallel-computing
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