Chen Qin
Engineering Manager II at Pinterest
United States
Summary
👤
Senior🎓
Top SchoolChen Qin is an engineering manager with 11 years of experience building large-scale stream and batch data platforms, currently leading Pinterest’s stream processing and near-real-time ML efforts. He grew a streaming practice from zero to 150+ use cases, built tier-0 ingestion into a data lake, and has been a visible industry speaker and Flink community contributor. Previously at Uber, AWS, and Facebook he helped introduce exact-once streaming, scaled distributed ML workloads, and delivered high-throughput financial ETL pipelines. Hands-on with both DevOps and backend work, he has contributed to prominent open-source projects like XGBoost and dmlc-core, improving CI, build reliability, and distributed ML stability. Known for blending technical depth with cross-organizational influence, he focuses on trustworthy fast signals and pragmatic platform SDKs that bridge research and production. He holds advanced CS degrees from Peking University and Dartmouth and started his career shipping distributed systems at research and startup labs.
11 years of coding experience
12 years of employment as a software developer
M.S, Computer Software Engineering, M.S, Computer Software Engineering at Peking UniversityB.S, Computer Science, B.S, Computer Science at Xi'an Jiaotong UniversityM.S, Computer Science, M.S, Computer Science at Dartmouth College
Github Skills (20)
gbm10
c-language10
python10
machine-learning10
bash10
distributed-systems10
travis-ci10
cicd10
cprogramming-language10
devops10
testing9
distributed-computing9
automations8
automation8
mpi8
Programming languages (5)
Github contributions (5)
dmlc/xgboost
Sep 2018 - Feb 2020
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:25 commits, 22 PRs, 212 comments in 1 year 5 months
Contributions summary:Chen primarily focused on improving the reliability and functionality of the XGBoost library, as well as the supporting infrastructure. They addressed build and test failures in the continuous integration (CI) pipeline, specifically related to model recovery tests and documentation generation. The user also worked on supporting larger clusters and optimizing the allreduce/broadcast operations. Additionally, they introduced and refined the use of the rabit_bootstrap_cache feature.
xgboostpythonflinkdaskdataflow
dmlc/dmlc-core
Mar 2019 - Jul 2019
A common bricks library for building scalable and portable distributed machine learning.
Role in this project:DevOps Engineer
DevOps EngineerContributions:7 commits, 9 PRs, 22 comments in 4 months
Contributions summary:Chen focused on maintaining and improving the build and testing infrastructure within the repository. They made changes to the Travis CI configuration, ensuring consistency across different operating systems and adjusting compiler settings. The user also addressed issues related to Python 3 compatibility and updated the linting scripts. Furthermore, they fixed environment detection in the MPI tracker, contributing to the overall stability and functionality of the distributed machine learning framework.
scalablebricksmachine-learningportabledistributed-machine-learning
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