Paden Tomasello
Research Manager, Facebook AI Research (FAIR)
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolPaden Tomasello is a research manager at Facebook AI Research with 11 years of experience building production-ready machine learning systems and leading research teams. He combines deep engineering chops—from CUDA kernels and sequence-criterion implementations for speech models to perception stacks for autonomous vehicles—with people management and product-focused execution. Prior roles span hands-on engineering and management at DeepScale and Graphistry, and he taught parallel software at UC Berkeley, reflecting strong systems and academic foundations. An active open-source contributor, his work on flashlight includes optimized C++ and CUDA implementations for CTC and ASG that improve sequence alignment and training performance. Based in Los Angeles, he brings a pragmatic research mindset that consistently moves novel ML algorithms toward scalable, deployable implementations.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.) Electrical Engineering and Computer Science, Bachelor of Science (B.S.) Electrical Engineering and Computer Science at University of California, Berkeley
Github Skills (9)
neural-network10
cuda10
machine-learning10
c-language10
deep-learning10
cprogramming-language10
autograd8
computer-vision4
mlops3
Programming languages (3)
Github contributions (5)
flashlight/flashlight
Nov 2020 - Oct 2021
A C++ standalone library for machine learning
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 30 PRs, 2 comments in 11 months
Contributions summary:Paden primarily contributes to the machine learning aspects of the project. Their work involves implementing and improving constrained Viterbi paths, including CUDA kernels for optimized performance, specifically for CTC (Connectionist Temporal Classification). The user also fixes issues in the alignment pipeline and contributes to unit tests demonstrating and validating the implemented algorithms. Further contributions demonstrate work in AutoSegmentationCriterion (ASG) which utilizes techniques common in speech recognition and other sequence-to-sequence tasks.
cppdeep-learningstandalone-libraryc-plus-plusmachine-learning
padentomasello/dotfiles
Jul 2015 - Sep 2021
Contributions:23 pushes, 1 branch in 6 years 3 months
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