Jialu Zhu
Engineering Manager at Meta
Palo Alto, California, United States
Summary
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Top SchoolJialu Zhu is an engineering manager in Palo Alto with eight years of experience building ML-driven recommendation and growth systems across Meta, Instagram, and Ant Group. She leads teams that operationalize machine learning at scale—most recently driving AI for Facebook Groups and video growth—and has deep hands-on experience with Kubernetes-native ML infrastructure from work on ElasticDL and task-oriented chatbots. Trained in computational and mathematical engineering at Stanford and with a physics BS from USTC, she combines strong modeling intuition with production-grade engineering. A long-time C++ user who also champions Python data work, she focuses on observability, debugging and maintainability in distributed training systems—improvements that often live in the core worker/master logic rather than the user-facing surface.
7 years of coding experience
6 years of employment as a software developer
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at University of Science and Technology of ChinaHigh School, High School at Beijing National Day SchoolMaster's Degree, Computational and Mathematical Engineering, Master's Degree, Computational and Mathematical Engineering at Stanford University
English, c++, python
Github Skills (7)
distributed-systems10
deep-learning10
tensorflow10
python10
kubernetes-pods9
logging9
kubernetes9
Programming languages (3)
Github contributions (5)
sql-machine-learning/elasticdl
May 2019 - Jul 2019
Kubernetes-native Deep Learning Framework
Role in this project:
ML Engineer
ML EngineerContributions:21 commits in 1 month
Contributions summary:Jialu primarily focused on enhancing the logging, debugging, and parsing logic within the ElasticDL framework. They made code changes to the worker and master components, indicating work on the core distributed deep learning infrastructure. Additionally, the user addressed linting issues and refactored the code to improve maintainability. These contributions centered on improving the training process and model deployment within a Kubernetes-native environment.
deep-learningmachine-learningmlopsdistributed-systemskubernetes
zhujl1991/argo
May 2019 - Jan 2020
ArgoProj: Get stuff done with Kubernetes.
Contributions:6 pushes, 4 branches in 8 months
argoprojk8skubernetes
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