Mark Kurtz
Member Of Technical Staff at Red Hat
Somerville, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolMark Kurtz is a pragmatic AI and software engineering leader with over a decade of experience building production-grade ML systems and leading teams from research to deployed products. As former CTO of Neural Magic and now Member of Technical Staff at Red Hat, he specializes in making AI faster, cheaper, and more accessible through open-source tooling and CPU-focused inference optimizations. He has guided hiring and management for large engineering organizations, launched thousands of CV/NLP models annually, and holds published papers and patents that bridge deep learning research and real-world engineering. His hands-on work on the deepsparse engine and SparseZoo integrations shows a penchant for shipping robust backend and MLOps improvements that materially improve benchmarking and model delivery. Based in Somerville, MA, he combines mechanical and robotics training with applied math to approach AI problems from both systems and algorithmic perspectives. Colleagues describe him as a founder-level technologist who turns complex research into practical, scalable products.
10 years of coding experience
15 years of employment as a software developer
Bachelor of Science (B.S.) Applied Mathematics, Bachelor of Science (B.S.) Applied Mathematics at Fontbonne UniversityMaster’s Degree Robotics Engineering, Master’s Degree Robotics Engineering at Washington University in St. Louis
English, Spanish
Github Skills (11)
machine-learning10
benchmark10
benchmarking10
performance-monitor10
inference10
performance-analysis10
onnx10
compilation10
python10
compile10
documentation8
Programming languages (3)
Github contributions (5)
neuralmagic/deepsparse
Dec 2020 - Jan 2023
Role in this project:
Backend Developer / MLOps Engineer
Backend Developer / MLOps EngineerContributions:280 reviews, 50 commits, 130 PRs in 2 years
Contributions summary:Mark primarily worked on refactoring and enhancing the DeepSparse Engine's core functionalities. This includes renaming and updating code documentation, as well as refactoring benchmarking code to use class object returns. The user also integrated sparsezoo compilation support and added benchmarking capabilities for improved model evaluation. Furthermore, they made improvements to the system's versioning and documentation processes.
llm-inferenceruntimetensorflowsparsificationmachinelearning
PIlotcnc/neural
Apr 2021 - Apr 2021
Contributions:36 commits in 13 days
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