Lin Yang
Software Engineer at Facebook
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolLin Yang is a seasoned software engineer with 14 years of experience building high-throughput, distributed systems for large-scale cloud and ads platforms. Based in Mountain View, he currently architects Ads ML infrastructure at Facebook, having previously led feature and ranking infrastructure teams and managed cloud infra at Baidu that supported hundreds of apps and billions of daily requests. He has deep expertise in storage, caching, and performance optimization—designing a cost-effective SSD-backed distributed cache and scaling services to petabyte-class datasets and 500K QPS. An active open-source contributor and maintainer-level engineer, Lin improved reliability and observability in the widely used twemproxy Redis/memcached proxy and added precision and testing support to the Caffe2 ML framework. He combines hands-on C/C++/systems work with platform design and team leadership, consistently turning complex production constraints into pragmatic, scalable solutions.
14 years of coding experience
7 years of employment as a software developer
Masters in Computer Science, Distributed Storage System, Masters in Computer Science, Distributed Storage System at Institute of Computing Technology Chinese Academy of SciencesBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Huazhong University of Science and Technology
Github Skills (15)
c1710
machine-learning10
logging10
caffe10
deep-learning10
c1110
python10
redis10
memcached10
testing10
numpy9
performance-optimization9
ai9
system-programming8
f8
Programming languages (9)
Github contributions (5)
twitter/twemproxy
Mar 2014 - Sep 2015
A fast, light-weight proxy for memcached and redis
Role in this project:
Back-end Developer
Back-end DeveloperContributions:69 commits, 4 PRs, 13 pushes in 1 year 5 months
Contributions summary:Lin primarily focused on enhancing the functionality of the twemproxy project, a fast, lightweight proxy for memcached and redis. Their contributions included adding detailed logging for requests, which involved modifying the core message handling code and incorporating time tracking metrics. They also refactored code to improve maintainability and addressed issues related to signal handling, specifically fixing potential deadlock scenarios within the signal handler. Furthermore, the user implemented safe string formatting functions to enhance the stability of the codebase.
light-weightredismemcacheproxylight
facebookarchive/caffe2
Jan 2018 - Feb 2018
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:ML Engineer
ML EngineerContributions:16 commits in 20 days
Contributions summary:Lin primarily contributed to testing and improving the `caffe2` deep learning framework. Their work involved adding tests for new features like `SparseLookup`, including various pooling methods like Sum and PositionWeighted. They also added tests for the `PairwiseDotProduct` layer and made changes to support half-precision floating point types, enhancing the framework's capabilities.
pytorchscalablecaffe2deep-learningml
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.