Bingxing Wang
Staff Software Engineer, Observability Engineering at LinkedIn
Morrisville, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolBingxing Wang is a Staff Software Engineer at LinkedIn with a decade of experience building large-scale observability, monitoring, and time-series systems, currently working on Geneva MDM and the “One Observability” vision. He blends deep backend engineering for metrics and alerting with hands-on network operations, firmware hacking, and hardware-level boot work, giving him a rare cross-layer perspective on reliability and performance. His open-source contributions include node-based simulation and VFX tooling (zeno), demonstrating strong mathematical and systems design skills beyond core infra. Previously he operated multi-site AnyCast networks and built identity and hybrid-cloud infrastructure, experience that informs his pragmatic approach to distributed systems. Bingxing pairs production-grade engineering at scale with a tinkerer’s curiosity—equally comfortable in TSDB internals, low-level firmware shims, and network architecture.
10 years of coding experience
8 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Indiana University Bloomington
Chinese, English
Github Skills (14)
rendering10
nodejs10
c-language10
cpp10
dataflow-programming10
cprogramming-language10
dataflow10
render10
flow-based-programming10
3d10
gfx9
simulation8
simulations8
opengl8
Programming languages (2)
Github contributions (5)
zenustech/zeno
Jun 2022 - Dec 2022
ZEn NOde system - a simulation & rendering engine in nodes
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 24 PRs, 11 pushes in 6 months
Contributions summary:Bingxing implemented and modified nodes within the Zeno node system, focusing on primitive operations and Houdini-style bending functionality. Their work involved modifying existing node implementations, creating new nodes such as those for circle creation, quaternion rotations, and matrix transformations. The user demonstrated knowledge of data structures and the use of external libraries like glm for mathematical calculations. This includes features to manipulate and visualize primitive data and the ability to implement terrain erosion features, indicating a strong focus on the visual effects aspect of the project.
rendering-enginevfxcppwebglnodes
bwangxxx/TensorFlow_Learning
Feb 2018 - Mar 2018
Contributions:86 pushes, 1 branch in 24 days
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