Sihan Wang
Staff Software Engineer at Pinterest
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSihan Wang is a Staff Software Engineer in the San Francisco Bay Area with 11 years of experience building scalable backend and distributed systems across companies like Pinterest, Anyscale, and VMware. She has deep expertise in deployment, autoscaling and runtime orchestration—demonstrated by contributions to Ray, a widely used open-source AI compute engine, where she refactored Serve’s deployment APIs, improved autoscaling policies, and added robust cleanup and test suites. Comfortable operating at the intersection of backend engineering and DevOps, she repeatedly drives reliability and maintainability improvements that make complex systems easier to operate at scale. Her background includes a Master’s in Computer Engineering from Columbia and hands-on experience shipping production systems in both startup and large-company contexts.
11 years of coding experience
8 years of employment as a software developer
Non-degree as an exchange student Computer Science, Non-degree as an exchange student Computer Science at University at BuffaloMaster’s Degree Computer Engineering, Master’s Degree Computer Engineering at Columbia UniversityBachelor's degree software engineering, Bachelor's degree software engineering at Beijing University of Technology
Chinese, English
Github Skills (8)
autoscaling10
deploying10
api10
python10
apidoc10
testing9
distributed-systems9
cicd8
Programming languages (6)
Github contributions (5)
ray-project/ray
Apr 2022 - Dec 2022
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:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:786 reviews, 53 commits, 205 PRs in 7 months
Contributions summary:Sihan primarily worked on implementing and refactoring Ray Serve's deployment and management APIs. They contributed to streamlining deployment clean-up, enhancing the ability to scale replicas, and refactoring deployment code for better organization. Furthermore, they also implemented changes related to autoscaling policies and integrated improvements into the system for more robust performance, along with the introduction of a test suite.
pythonconsistsruntimetensorflowserving
sihanwang41/ray
Apr 2022 - Jan 2024
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:1202 pushes, 191 branches, 1 comment in 1 year 9 months
apirayscalabledistributed-applicationshyperparameter
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