Philip Bontrager
Machine Learning Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolPhilip Bontrager is a machine learning engineer with nine years of experience building production-grade AI systems, currently contributing to PyTorch tooling at Meta. He blends academic rigor from advanced CS studies with hands-on research roles at startups and industry, focusing on generative models, reinforcement learning, and evolutionary strategies. At Meta he works on PyTorch projects like torchtune and torchforge, and his GitHub contributions show attention to codebase maintainability and clean architecture for model tooling. Previously he led model and training software development for e-commerce search and applied AI to energy simulations, demonstrating an ability to move research into impactful products. Based in New York, he brings both research depth and practical engineering chops, with a knack for reorganizing complex codebases to streamline future development.
9 years of coding experience
6 years of employment as a software developer
Goshen CollegeMaster's degree, Computer Science, Master's degree, Computer Science at New York University - Polytechnic School of Engineering
Github Skills (10)
llama10
model-building10
transformer-models10
pytorch10
deep-learning10
code-organization10
python10
machine-learning9
api8
apidoc8
Programming languages (4)
Github contributions (5)
pytorch/torchtune
Mar 2024 - Apr 2025
PyTorch native post-training library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 301 reviews, 83 PRs in 1 year
Contributions summary:Philip primarily contributed to the internal structure and organization of the repository. They restructured the repository layout based on an RFC and made various changes to imports and file locations to reflect the new layout. The changes included modifications to several Python files, primarily relating to the Llama2 model, and involved moving or updating references to different components within the project, and also added model and tokenizer getters and argument parser updates. These adjustments indicate an effort to improve the codebase's maintainability and potentially streamline future development workflows.
pbontrager/GenerativePlayingNetworks
Mar 2019 - Feb 2020
Contributions:45 commits, 6 pushes in 11 months
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