Ian Stenbit
ML Software Engineer at NVIDIA
Boulder, Colorado, United States
Summary
🤩
Rockstar🎓
Top SchoolIan Stenbit is an ML software engineer with 11 years of experience building scalable AI infrastructure, currently contributing to NVIDIA's AI stack from Boulder. He spent four years at Google working on large-scale systems and now focuses on production-ready machine learning tooling and computer vision at the intersection of research and engineering. Ian is an active Keras contributor, improving core libraries and KerasCV—adding pretrained weights, GPU-accelerated testing, efficient ImageNet loading, and novel augmenter layers that help bridge research models to industry workflows. His background spans real-time SLO enforcement, high-throughput data pipelines, and applied cybersecurity research, reflecting a knack for turning complex systems into reliable production services. Ian holds MS and BS degrees in Computer Science from Southern Methodist University and brings both deep model-evaluation expertise (confusion-matrix metrics, precision/recall) and a practical focus on performance and reproducibility.
11 years of coding experience
6 years of employment as a software developer
Master of Science (MS), Computer Science, Master of Science (MS), Computer Science at Southern Methodist University
Stackoverflow
46reputation
4kreached
3answers
0questions
Github Skills (22)
python10
evaluation10
data-science10
machine-learning10
keras10
eval10
deep-learning10
tensorflow10
neural-network10
computer-vision10
image-augmentation10
unit-testing9
trainings9
modeling9
prediction6
Programming languages (7)
Github contributions (5)
keras-team/keras-cv
Aug 2022 - Jan 2023
Industry-strength Computer Vision workflows with Keras
Role in this project:
ML Engineer
ML EngineerContributions:18 releases, 1270 reviews, 115 commits in 5 months
Contributions summary:Ian primarily contributed to the KerasCV library's computer vision capabilities. Their work included implementing flag-controlled learning rates, fixing docstrings, adding support for GPU-accelerated testing, incorporating script versions into training history scripts, and adding and refining pre-trained model weights, specifically for ResNet50V2, DenseNet201, and EfficientNetV2B0/B1. They also introduced an augmenter layer and made improvements to the image loading processes, with an emphasis on efficient ImageNet dataset loading.
visiondeep-learningcomputer-visionindustrystrength
keras-team/keras
Jul 2022 - Sep 2022
Deep Learning for humans
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:5 reviews, 6 commits, 3 PRs in 1 month
Contributions summary:Ian primarily contributed to the Keras core library, focusing on enhancements and bug fixes related to machine learning and deep learning applications. Their work included fixing issues in the NASNet model implementation, adding tests for applications with custom input shapes, and supporting the `class_weight` parameter for multi-dimensional data in `model.fit`. The user also implemented several confusion matrix metrics, including Precision and Recall, showing proficiency in model evaluation and performance analysis within the Keras framework.
pythondata-sciencedeep-learningneural-networksmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.