Lixin Wei
Software Engineer at Metabit Trading (Qianxiang Investment)
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolLixin Wei is a software engineer with a decade of experience specializing in low-latency, high-throughput data processing and C++ systems engineering. He built two data analytics/streaming frameworks from scratch and contributed to Ray, a widely used distributed AI compute engine, improving runtime stability with memory estimation fixes, checkpointing, and actor restart features. At Ant Group he helped scale recommendation and online learning systems and developed Flink connectors and streaming operators, then moved to Quant trading to architect real-time stream and batch processing for Metabit Trading. Comfortable across core distributed runtime, streaming, and online optimization domains, he brings a practical focus on scalability and fault tolerance. Colleagues value him for shipping production-grade frameworks and tackling subtle reliability challenges that often lurk behind performance wins.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering - BE, Computer Science, Bachelor of Engineering - BE, Computer Science at Hangzhou Normal University
English, Chinese
Stackoverflow
453reputation
45kreached
4answers
20questions
Github Skills (18)
python10
distributed-systems10
fault-tolerance10
ray10
java9
parallel-computing9
javas9
deep-learning8
machine-learning8
pytorch7
tensorflow7
sass6
laravel6
llm6
npm6
Programming languages (16)
Github contributions (5)
ray-project/ray
Jan 2020 - Jun 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
Back-end DeveloperContributions:165 reviews, 55 commits, 82 PRs in 2 years 6 months
Contributions summary:Lixin primarily contributed to the Ray project's core functionalities, specifically focusing on improving the distributed runtime and associated AI libraries. Their work included fixing memory estimation issues and supporting multiple downstream collectors. Furthermore, the user added functionality related to fault tolerance, including checkpointing and actor restart mechanisms. These changes demonstrate a focus on improving stability, performance, and core functionality within the Ray framework.
aimachine-learningraydistributedparallel
lixin-wei/acm-resolver
Dec 2017 - Mar 2019
ICPC系列竞赛通用滚榜程序/Competitive Programming Rank Visualizer/Rank Resolver
Contributions:10 commits, 1 PR, 16 pushes in 1 year 3 months
acticpcbrowserresolverclient-side
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