Bora Oztekin
Senior Cloud Software Engineer at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolBora Oztekin is a Senior Cloud Software Engineer at NVIDIA with 11 years of engineering experience and concurrent MS studies in Computer Science at Stanford. He blends cloud and data-science expertise—advancing production cloud services at NVIDIA after a progression from data science roles—while contributing to graph deep learning research from his undergraduate work at Texas A&M’s DIVE lab. Bora has hands-on experience implementing research-grade models (e.g., JT-VAE tree decomposition utilities) and modernizing codebases for Python 3 compatibility in open-source graph ML tooling. Comfortable across AWS-backed data pipelines, ML frameworks, and scalable cloud systems, he brings both research rigor and production discipline to ML infrastructure. Based in California, he pairs statistical foundations with network-science instincts to turn complex graph and geospatial problems into deployable solutions.
11 years of coding experience
4 years of employment as a software developer
High School, High School at Plano West Senior High SchoolBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Texas A&M UniversityHigh School, High School at Troy High SchoolMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
Stackoverflow
248reputation
7kreached
4answers
11questions
Github Skills (17)
pytorch10
python10
deep-learning10
machine-learning9
3d8
graph8
3d-graphics8
self-supervised-learning7
html6
sql6
css6
overlay6
yelp6
django6
java6
Programming languages (3)
Github contributions (5)
divelab/DIG
Mar 2021 - Jul 2021
A library for graph deep learning research
Role in this project:
ML Engineer
ML EngineerContributions:34 commits, 16 pushes, 2 comments in 4 months
Contributions summary:Bora's commits primarily focus on adding utilities and implementing tree decomposition from the JT-VAE paper within the context of a graph deep learning research library. The user appears to be developing code related to the JT-VAE model, including modifications to support Python 3.7 and reorganizing components. The commits also include the setup script, suggesting involvement in the model's environment configuration.
explainable-mlpytorchdataminingdeep-learninggraph-deep-learning
borao/judge-variance
Jun 2017 - Jul 2020
Contributions:1 PR, 23 pushes, 1 comment in 3 years 1 month
javascriptjudgesscoreshandycalculate
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