Nathan Raw
Senior Machine Learning Engineer at Splice
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolNathan Raw is a Senior Machine Learning Engineer based in Austin with eight years of experience building production-ready ML systems and developer tooling. He has a strong open-source track record at Hugging Face and PyTorch Lightning, contributing image and video pipelines to transformers, expanding datasets for computer vision, and improving Trainer/DataModule ergonomics. At Splice he continues applying that blend of research and engineering to product-focused ML, and his prior roles span research engineering, AI product management, and applied data science at Grid AI and PwC. Nathan combines hands-on PyTorch/PyTorch Lightning expertise with MLOps skills—adding model hub integration and pretrained model workflows—to bridge models from experiments to deployable artifacts. Colleagues will notice his knack for refactoring and modularizing complex codebases to improve reuse and reliability, and his GitHub bio’s self-effacing “pretending to program” belies a consistent record of impactful, widely used contributions.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.) Management Information Systems General, Bachelor of Science (B.S.) Management Information Systems General at Rochester Institute of Technology - Saunders College of Business
Stackoverflow
23reputation
3kreached
1answer
1question
Github Skills (41)
continuous-deployment10
transformers10
pytorch10
hub10
python10
pre-trained-model10
data-science10
classification10
image-classification10
huggingface-hub10
machine-learning10
pytorch-lightning10
ml-deployment10
autoencoder10
datasets10
Programming languages (11)
Github contributions (5)
huggingface/huggingface_hub
Jul 2021 - Nov 2022
The official CLI and Python client for the Hugging Face Hub.
Role in this project:
Backend Developer
Backend DeveloperContributions:119 reviews, 19 commits, 35 PRs in 1 year 4 months
Contributions summary:Nathan primarily contributed to the development of mixins for the Hugging Face Hub client, specifically focusing on integration with PyTorch and Keras models. They added functionalities like `_from_pretrained` and `_save_pretrained` hooks, allowing for custom saving and loading logic. These efforts involved refactoring existing code, adding new features, and updating documentation. The user also integrated the `hf_hub_download` functionality, streamlining the model download process.
clihuggingfacepythonmodel-hubmachine-learning
huggingface/pytorch-image-models
Sep 2021 - Jan 2023
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Role in this project:MLOps Engineer
MLOps EngineerContributions:5 reviews, 30 commits, 11 PRs in 1 year 4 months
Contributions summary:Nathan focused on integrating the repository with the Hugging Face Hub, enabling model saving, versioning, and deployment. Their contributions involved adding the functionality to push trained models, along with their configurations, to the hub. This included implementing a model card generation and a cleanup of existing related code. They also made changes to the helper and models to accommodate changes from the master branch.
efficientnetinferencemobilenetpytorchresnet
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