Peixuan Liu
SDE II at Amazon
Greater Seattle Area United States
Summary
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Rockstar🎓
Top SchoolPeixuan Liu is an SDE II with a decade of experience building large-scale, privacy-preserving systems and personalization infrastructure in the Greater Seattle Area. At Meta, Peixuan was a founding contributor to both ExecuTorch—an open-source PyTorch framework for fast, secure on-device inference—and the Private Computation project, one of the first real-world deployments of large-scale multi-party computation for ad measurement. Now at Amazon, they focus on personalization and Customer 360, bringing production-grade backend engineering and system consolidation expertise. Their open-source contributions include refactoring privacy-focused back-end components in Facebook’s FBPCS repository, demonstrating a knack for improving maintainability across distributed Python services. Beyond engineering, Peixuan has taught and mentored students in core CS topics and led campus organizations, reflecting a blend of technical depth and community-oriented leadership.
10 years of coding experience
7 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Southern CaliforniaUniversity of California, San Diego
Chinese, English
Github Skills (6)
back-end-development10
python10
refactoring9
data-modeling9
api8
apidoc8
Programming languages (2)
Github contributions (5)
facebookresearch/fbpcs
Aug 2021 - Mar 2022
FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:88 commits, 54 PRs in 6 months
Contributions summary:Peixuan primarily focused on refactoring and modifying back-end code, particularly within the `fbpmp` directory, which appears to be related to privacy-focused computation. Their work involved renaming classes, updating service configurations, and incorporating new attributes into data models. These changes were driven by the need to consolidate similar classes used by the Private Lift (PL) and Private Attribution (PA) systems, indicating efforts towards code maintainability and system consolidation. The user demonstrated an understanding of the project's architecture and data flow by addressing changes across multiple Python files.
outcomeadvertisermulti-party-computationmulti-partycomputation
Olivia-liu/executorch-1
Nov 2023 - Dec 2024
End-to-end solution for enabling on-device AI across mobile and edge devices for PyTorch models
Contributions:175 pushes, 60 branches in 1 year 1 month
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