Eric Chen
Cellular Platform Engineer
Chaoyang District, Beijing, China
Summary
👤
Senior🎓
Top SchoolEric Chen is a Cellular Platform Engineer with 11 years of hands-on experience in baseband and PHY development, currently contributing to cellular platform work at Apple from Beijing. He has deep expertise in beamforming and 5G NR features from roles at Ericsson and ZTE, where he solved customer node issues and implemented MU beamforming and DMRS support for multi-antenna RUs. Eric combines low-level DSP/baseband engineering with practical DevOps skills—he has contributed Hadoop/YARN deployment scripts for the high-profile XGBoost project to streamline distributed ML jobs. Trained at Harbin Institute of Technology (BEng and MEng), he blends solid academic grounding with a track record of troubleshooting complex radio and platform integrations in production environments. He is known for bridging firmware-level development and system-level deployment to deliver reliable, scalable cellular features.
11 years of coding experience
4 years of employment as a software developer
Master’s Degree, Electrical and Electronics Engineering, Master’s Degree, Electrical and Electronics Engineering at 哈尔滨工业大学Bachelor’s Degree, Electrical, Electronic and Communications Engineering Technology/Technician, Bachelor’s Degree, Electrical, Electronic and Communications Engineering Technology/Technician at Harbin Institute of Technology
English, French, German
Github Skills (16)
yarn-berry10
yarn210
sh10
script10
distributed-systems10
shell10
hadoop10
scripting10
yarn310
yarnpkg10
machine-learning9
gbm8
python8
devops7
automations7
Programming languages (1)
Github contributions (5)
dmlc/xgboost
Jan 2015 - Aug 2015
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:
DevOps Engineer
DevOps EngineerContributions:9 commits, 5 PRs in 6 months
Contributions summary:Eric primarily contributed to the project by modifying and creating scripts related to running XGBoost jobs on Hadoop YARN. These changes involved updating `rabit_yarn.py` to configure job parameters like memory, timeout, and CPU cores. They also created and modified shell scripts like `run_yarn_mushroom.sh` to streamline the job submission process, including setting up data in HDFS and retrieving results. This work indicates a focus on deploying and managing the execution of XGBoost models within a Hadoop environment.
daskdataflowgbmgradient-boostinghadoop
wachaong/algo-features
Oct 2014 - Feb 2015
Contributions:13 commits in 3 months
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