Samantha Andow
Software Engineer at Financial Company
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolSamantha Andow is a software engineer with 10 years of experience building scalable systems and machine learning infrastructure, currently at a financial firm in New York. She spent five years at Facebook and has deep open-source contributions to PyTorch and functorch, improving core autodiff, Jacobian/VJP support, and batching rules for high-performance ML workloads. A Harvey Mudd CS graduate and former research lead and tutor, she combines strong engineering with a knack for teaching and data-driven curriculum improvement. Her work on core PyTorch operations and tests reflects a focus on numerical correctness and production-ready ML tooling—skills she pairs with practical cloud and container experience developed during a Microsoft internship.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Science, Major GPA - 3.61/4.0, Bachelor's degree, Computer Science, Major GPA - 3.61/4.0 at Harvey Mudd CollegeHigh School Diploma, Overall GPA - 3.8/4.0, High School Diploma, Overall GPA - 3.8/4.0 at San Francisco University High School
English
Github Skills (11)
tensorrt10
pytorch10
machine-learning10
tensor10
deep-learning10
tensorflow10
operation10
python10
gradient10
unit-testing9
backpropagation8
Programming languages (5)
Github contributions (5)
pytorch/functorch
Sep 2021 - Sep 2022
functorch is JAX-like composable function transforms for PyTorch.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:70 reviews, 200 commits, 153 PRs in 1 year
Contributions summary:Samantha primarily contributed to the `functorch/functorch` repository by implementing new features and improving the existing code related to function transformations for PyTorch. Their work focused on adding arguments and support for Jacobian and VJP, in addition to expanding the batching rules for operations like grid sampling. They also contributed to adding support for in-place random operations.
pytorchgradientshessianscomposablejax
pytorch/pytorch
Aug 2021 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:ML Engineer
ML EngineerContributions:381 reviews, 800 commits, 115 PRs in 1 year 4 months
Contributions summary:Samantha contributed to the core PyTorch library, focusing on machine learning and deep learning. The commits demonstrate work on the implementation and refinement of operations within the PyTorch framework. These changes include supporting forward over reverse in the embedding layer and addressing issues related to backward pass correctness. Additionally, the user added comprehensive tests to validate the functionality of these operations.
pythongpu-accelerationdeep-learninggpunumpy
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