John Clow
Senior Software Engineer at SafetyKit
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Clow is a Senior Software Engineer with 11 years of experience building high-performance backend systems and ML tooling from startups to large tech companies. He combines deep systems and ML expertise—Python, C/C++, PyTorch and TensorFlow—with practical production engineering, having optimized TorchScript passes that sped up BERT by 25% and contributed shape analysis improvements to the core PyTorch repo. At Facebook he architected measurement platforms and release tooling, and at Canopy he led a team migrating complex business logic from Postgres stored procedures to typed Python with SQLAlchemy and Pydantic. He’s FINRA-licensed (Series 79 and 24) which gives him uncommon domain fluency for engineering in regulated finance and lending products. A former Stanford CS instructor who taught CS231n and CS224n, he pairs academic depth in neural networks with hands-on delivery and mentoring. Now based in San Francisco, he’s driving ML and backend work at SafetyKit while continuing active open-source contributions.
11 years of coding experience
7 years of employment as a software developer
St. Paul's SchoolBachelor's Degree Economics, Bachelor's Degree Economics at UC Santa BarbaraMaster Computer Science, Master Computer Science at Stanford University
Github Skills (9)
pytorch10
tensor10
python10
profiling10
autograd10
cprogramming-language9
machine-learning9
c-language9
deep-learning9
Programming languages (10)
Github contributions (5)
pytorch/pytorch
May 2021 - Aug 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:208 reviews, 266 commits, 107 PRs in 1 year 3 months
Contributions summary:John primarily contributed to the PyTorch repository by implementing and modifying shape functions, which are crucial for the library's automatic differentiation and symbolic shape analysis capabilities. Their work involved adding support for new features like tensor metadata and scalar values within the profiling system. They also addressed documentation issues and refactored release note scripts. The contributions demonstrate a focus on improving the performance, debugging capabilities and internal workings of the PyTorch framework.
pythongpu-accelerationdeep-learninggpunumpy
Gamrix/cs231n_proj
May 2017 - Jul 2017
Contributions:91 commits, 38 pushes, 1 comment in 1 month
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.