Tugsbayasgalan Manlaibaatar
Software Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolTugsbayasgalan Manlaibaatar is a software engineer in New York with 10 years of experience focused on machine learning, infrastructure, and compilers. Currently on the PyTorch compilers team at Meta, he has contributed non-trivial fixes to PyTorch’s serialization, tensor functionalization, and tracing/export internals—work that improves reliability for a widely used deep learning framework. His background includes research at MIT CSAIL’s COMMIT Compiler Group optimizing parallel graph algorithms and practical systems work building cost-saving, zone-aware load balancing at Samsara. He combines strong academic credentials (MEng and BS from MIT) with hands-on production engineering, bridging compiler theory and large-scale ML system demands. Colleagues describe him as someone who surfaces subtle correctness issues in complex codepaths and turns them into robust, deployable solutions.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science and Engineering, 4.7/5.0, Bachelor of Science - BS, Computer Science and Engineering, 4.7/5.0 at Massachusetts Institute of TechnologySant school
Mongolian, English
Github Skills (11)
pytorch10
data-serialization10
tensor10
tensorflow10
python10
serialization10
autograd9
code-optimization9
test-automation8
deep-learning5
machine-learning5
Programming languages (4)
Github contributions (5)
pytorch/pytorch
Nov 2020 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1487 reviews, 440 commits, 464 PRs in 2 years 2 months
Contributions summary:Tugsbayasgalan contributed to the PyTorch library by fixing bugs related to serialization and subclass constructors. They addressed issues within the functionalization of tensors and improved error messages for incorrect API usage. Their work involved modifications to the export functionality, specifically focusing on handling tensor subclasses, code generation, and improving the overall reliability of the tracing and serialization processes within the PyTorch framework. They added the support for the auto functionalize operations, which require non-trivial changes to the internal mechanism.
pythongpu-accelerationdeep-learninggpunumpy
tugsbayasgalan/pytorch
Oct 2020 - Mar 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:348 pushes, 74 branches in 4 years 5 months
pythongpu-accelerationdeep-learninggpuacceleration
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