Li Wang
General Manager - GPU Optimization & LLM Engineering
Singapore
Summary
👤
Senior🎓
Top SchoolLi Wang is a GPU-focused engineering leader with 12 years of experience designing and optimizing large-scale training and inference platforms for LLMs and CV workloads. Currently General Manager for GPU Optimization & LLM Engineering at Rakuten in Singapore, he previously led AI platform and GPU inference teams at Shopee and YITUTech, driving production-ready distributed training and online ranking systems. His background spans research-grade distributed systems—he holds a Ph.D. in Computer Science—and practical orchestration of GPU clusters and performance-critical components. An active open-source contributor, he improved core performance and observability in projects like Apache Storm by optimizing queue and timer subsystems. Colleagues know him for blending systems-level rigor with product focus to shorten ML iteration cycles and reduce inference costs. Outside work he’s known to cook with his partner, a small but telling hint at his collaborative and detail-oriented nature.
12 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at East China Normal UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Guizhou University
English, Chinese
Stackoverflow
1reputation
479reached
1answer
1question
Github Skills (12)
javas10
distributed-systems10
apache-storm10
java10
metric10
concurrency10
performance-optimization9
algorithms8
data-structures8
algorithm8
testing8
data-structure8
Programming languages (10)
Github contributions (5)
apache/storm
Aug 2015 - Nov 2016
Apache Storm
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 commits, 8 PRs, 39 comments in 1 year 3 months
Contributions summary:Li primarily contributed to the Apache Storm project by implementing improvements and optimizations to the `DisruptorQueue` component, including the addition of metrics like arrival rate and sojourn time. They addressed a typo and a code logic error in the resource scheduling components. Furthermore, the user refactored the timer logic to improve responsiveness. Overall, their contributions focused on enhancing the system's performance, monitoring capabilities, and core functionalities.
apachebig-datastormjavaapache-storm
wangli1426/b-tree
Jun 2017 - Sep 2017
Contributions:43 commits, 34 pushes, 2 branches in 3 months
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