Laith Sakka
Staff Research Scientist at Meta
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolLaith Sakka is a Seattle-based software engineer with nine years of experience and a current role at Meta. He brings deep ML systems expertise, contributing to PyTorch's Dynamo by implementing and fixing automatic differentiation and optimization features. His work tackled tricky areas like operator support and handling of random calls in inlined code, and he pairs feature work with thorough test coverage to ensure correctness. Laith combines production-grade engineering at scale with active open-source contributions that bridge research frameworks and GPU-accelerated deployment.
10 years of coding experience
7 years of employment as a software developer
Bachelor's degree Computer Engineering, Bachelor's degree Computer Engineering at Princess Sumaya University for TechnologyDoctor of Philosophy (PhD) Computer Engineering, Doctor of Philosophy (PhD) Computer Engineering at Purdue University
English, Arabic
Github Skills (12)
amazon-dynamodb10
pytorch10
machine-learning10
deep-learning10
dynamodb10
python10
aws-dynamodb10
autograd10
testing9
neural-network9
tensor8
gpu7
Programming languages (7)
Github contributions (5)
pytorch/pytorch
Jan 2024 - Apr 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:352 reviews, 226 PRs, 1824 pushes in 1 year 2 months
Contributions summary:Laith primarily contributed to the PyTorch library, focusing on the Dynamo component. Their work involved implementing and fixing functionalities related to automatic differentiation and optimization. This includes addressing issues with operator support, improving the handling of random calls within inlined code, and extending support for built-in callables. Additionally, the user contributed tests to ensure the correctness of these features.
pythongpu-accelerationdeep-learninggpunumpy
laithsakka/pytorch
Jan 2024 - Jan 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:174 pushes, 34 branches in 1 year
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