Rachel Oberman
AI Solutions Architect at NVIDIA
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolRachel Oberman is an AI Solutions Architect at NVIDIA with nine years of hands-on experience building and deploying ML solutions across industry and research. She brings practical expertise from a four-year tenure at Intel—contributing optimized oneAPI AI Analytics Toolkit samples—and from founding and directing the geoLab research lab where she translated geospatial problems into deployable data science workflows. With an MS in Computer Science from Columbia and a BS in Data Science from William & Mary, she bridges academic rigor and production engineering. Rachel combines model-building, performance optimization, and developer enablement to help teams adopt AI efficiently. A former Team USA figure skater, she applies elite-athlete discipline and creativity to complex technical challenges.
9 years of coding experience
7 years of employment as a software developer
High School Diploma High School Diploma, High School Diploma High School Diploma at Watchung Hills Regional High SchoolMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Columbia UniversityBachelor of Science - BS Computer Science Data Science, Bachelor of Science - BS Computer Science Data Science at William & Mary
English, Spanish
Github Skills (10)
xgboost10
machine-learning10
oneapi10
python10
ai10
scikit-learn9
scikit9
pandas9
modin8
jupyter7
Programming languages (6)
Github contributions (5)
oneapi-src/oneAPI-samples
Oct 2020 - Mar 2022
Samples for Intel® oneAPI Toolkits
Role in this project:
ML Engineer
ML EngineerContributions:13 reviews, 10 commits, 14 PRs in 1 year 5 months
Contributions summary:Rachel's commits primarily focused on integrating and demonstrating the use of Intel's oneAPI AI Analytics Toolkit samples within the repository. This included adding and modifying samples for Modin, XGBoost, and daal4py, as well as updating README files and sample.json files to reflect changes and improvements. The contributions aimed to provide examples of machine learning model training and prediction using optimized Intel libraries.
numbatoolkitsswrepoaplpower-management
raoberman/oneAPI-samples
Sep 2020 - Mar 2022
Samples for Intel oneAPI toolkits
Contributions:86 pushes, 2 branches in 1 year 6 months
intelintel-oneapi-toolkitsoneapitoolkitsrelational-algebra
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