Alan Guo
Senior Software Engineer at Meta
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlan Guo is a Senior Software Engineer with nine years of experience building low-latency, highly available systems for consumer and infrastructure products. He has shipped device arbitration and multi-device features at Amazon Lab126, scaled services at TikTok, and now contributes to large-scale systems at Meta, combining deep code-level curiosity with pragmatic delivery. An active contributor to the Ray project, he improved the autoscaler and file-mount syncing—work that touches orchestration for distributed AI workloads. Comfortable leading designs in team settings or executing independently, he prioritizes customer-facing quality and scalable defaults. Based in Mountain View with a dual ECE and CS background from Duke, he brings hardware-informed systems thinking to cloud and distributed software challenges.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.S.) Electrical and Computer Engineering (ECE) and Computer Science, Bachelor of Science (B.S.) Electrical and Computer Engineering (ECE) and Computer Science at Duke UniversityHigh School Diploma, High School Diploma at Foothill High School
English, Chinese, Spanish
Github Skills (8)
autoscaling10
configuration-management10
rsync10
file-transfer10
devops10
python10
build-automation9
distributed-systems9
Programming languages (9)
Github contributions (5)
ray-project/ray
Jun 2020 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:680 reviews, 89 commits, 216 PRs in 2 years 7 months
Contributions summary:Alan focused on improving the Ray autoscaler, implementing default configuration options, and adding features related to file mounting, including the ability to continuously sync file mounts. They extracted configuration preparation actions, introduced options for continuous file synchronization from the head node to worker nodes, and added options related to rsync and excluding/filtering files. Furthermore, they contributed to refactoring and standardizing configurations within the project.
aimachine-learningraydistributedparallel
alanwguo/ray
Jun 2020 - Feb 2026
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:6 PRs, 564 pushes, 177 branches in 5 years 8 months
hyperparameter-tuningreinforcement-learningrllib
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