Benjamin Chislett
Senior Systems Software Engineer at NVIDIA
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolBenjamin Chislett is a Senior Systems Software Engineer with 8 years of experience specializing in GPU optimization for machine learning and differentiable parallel programs. He has deep systems expertise in CUDA/PTX, TVM (C++/Python), GPU physics simulation (CUDA/C++ and Julia), and full ML/DB engineering, and is currently applying that work at NVIDIA after a senior engineering stint at CentML. Benjamin has contributed practical performance benchmarks to Activeloop's Deeplake—focusing on data loading, random access, and compression—and pairs that real-world performance focus with a strong academic foundation (BSc Computer Science, 3.96) and multiple research internships and TA roles at the University of Toronto. Comfortable across the stack from low-level compilers to React+TypeScript, he combines research rigor with production-grade engineering to squeeze performance out of modern ML pipelines.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.96, Bachelor of Science - BS, Computer Science, 3.96 at University of Toronto Scarborough
Stackoverflow
447reputation
142kreached
1answer
7questions
Github Skills (19)
pytorch10
benchmark10
python10
data-science10
machine-learning10
benchmarking10
numpy10
datasets10
tensorflow210
tensorflow10
performance-optimization10
computer-vision8
image-processing8
jestjs6
fortran6
Programming languages (11)
Github contributions (5)
activeloopai/deeplake
Jan 2021 - Aug 2021
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:490 reviews, 227 commits, 70 PRs in 6 months
Contributions summary:Benjamin contributed significantly to the project by implementing various benchmarks focused on data loading, access, and compression performance. They developed benchmarks utilizing both PyTorch and TensorFlow for dataset iteration, random data access, and image compression speed, demonstrating a focus on optimizing data processing pipelines for AI/ML applications. The commits show a strong understanding of data loading and prefetching techniques, as well as an emphasis on measuring performance for different data access patterns.
pythonreal-time-datadata-streamjupyter-notebookvisualize
benchislett/Pomorello
Aug 2019 - May 2021
Pomodoro tracker for Trello cards
Contributions:279 commits, 4 PRs, 251 pushes in 1 year 9 months
reactpomodoro-trackerpomodorotrackertrello
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