John Biebelhausen
Director, OEM Marketing at NVIDIA
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Biebelhausen is a seasoned go-to-market and product marketing leader with 10+ years in senior roles and over three decades of experience driving adoption of datacenter and cloud infrastructure solutions. As NVIDIA's Director of OEM Marketing based in Austin, he translates complex AI and hardware capabilities into measurable business outcomes and partner programs that accelerate revenue. He blends deep enterprise marketing discipline from IBM, Lenovo, and Dell with hands-on technical fluency—evidenced by meaningful open-source contributions to RAPIDS/cuML that improved GPU-accelerated data pipelines and model serialization testing. Known for building cross-functional alignment across sales, product and engineering, he excels at launching differentiated offerings and packaging technical value for OEM partners. His background in finance and strategic product planning gives him a pragmatic, metrics-driven approach to positioning emerging AI infrastructure in competitive markets.
10 years of coding experience
32 years of employment as a software developer
Master’s Degree, Finance, Master’s Degree, Finance at Colorado State UniversityBachelor's Degree, Economics and Finance, Bachelor's Degree, Economics and Finance at Kent State University
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (14)
cudf10
machine-learning10
feature-engineering10
data-cleaning10
pytest10
python10
gpu9
jupyter-notebook9
pandas9
machine-learning-algorithms9
cuda9
numpy8
compiler7
rapids6
Programming languages (7)
Github contributions (5)
rapidsai/cuml
Jan 2020 - Feb 2022
cuML - RAPIDS Machine Learning Library
Role in this project:
ML Engineer & Test Automation Engineer
ML Engineer & Test Automation EngineerContributions:31 reviews, 91 commits, 37 PRs in 2 years
Contributions summary:John contributed to the `cuml` repository by adding and testing new features for machine learning models. Specifically, they focused on implementing the `getstate` and `setstate` methods for machine learning models, enabling model serialization and deserialization. Additionally, the user introduced and modified tests for various machine learning algorithms, including k-neighbors classifiers and regressors. The changes also involved style and code improvements to existing test files, making the code cleaner and more maintainable.
cudacumlnvidiadata-sciencegpu
rapidsai-community/notebooks-contrib
Aug 2019 - May 2020
RAPIDS Community Notebooks
Role in this project:Data Scientist
Data ScientistContributions:13 commits, 4 PRs, 3 comments in 9 months
Contributions summary:John made changes to an end-to-end notebook for the NYC taxi dataset, likely for the purpose of data analysis, cleaning, and model building. Their contributions included modifying data cleaning steps, implementing the use of Dask-cuDF for handling data, and modifying the code to take advantage of GPU acceleration. The changes also involved working with datetime columns and implementing feature engineering steps.
jupyter-notebooknotebooksrapids
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