Eric Ge
Research Engineer at Google DeepMind
Boston, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolEric Ge is a research engineer with eight years of experience building machine learning infrastructure, site reliability tooling, and distributed systems, currently working on agentic coding evaluations at Google DeepMind. He has driven production ML orchestration at Google—contributing to TFX and Kubernetes-based deployment tooling—and helped benchmark large-model and agentic behaviors on Google Cloud. Comfortable across SRE, backend systems, and MLOps, he pairs hands-on kernel-level contributions (container entrypoints and dag runners) with language-level enhancements (core P language data structures). Based in Boston and Berkeley-educated in CS, he combines rigorous engineering with open-source sensibilities, notably contributing to the widely used TFX project to make ML pipelines more deployable and secure.
8 years of coding experience
5 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of California, BerkeleyHigh School Diploma, High School Diploma at The High School Affiliated to Renmin University of ChinaSummer Program, Summer Program at Phillips Exeter Academy
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (17)
kubernetes10
docker10
tfx10
dockers10
kubernetes-pods10
orchestration10
orchestra10
devops10
model-checking9
asynchronous9
gcp9
c-programming9
distributed-systems9
yaml9
cprogramming-language9
Programming languages (7)
Github contributions (5)
p-org/P
Oct 2019 - Mar 2020
The P programming language.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:19 commits, 6 PRs, 24 pushes in 4 months
Contributions summary:Eric primarily focused on enhancing the P programming language's core functionality, specifically implementing and refining set data structures. They addressed dynamic exit code inference in test cases, resolved regression test issues, and refactored test structures. Their contributions involved substantial modifications to the PrtValues.c, PrtCodeGenerator.cs, and other supporting files, demonstrating a strong understanding of the language's internal workings.
model-checkingcoq-librarytlaformal-verificationcompiler
tensorflow/tfx
Jun 2020 - Aug 2020
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:DevOps Engineer
DevOps EngineerContributions:1 review, 122 commits, 6 PRs in 2 months
Contributions summary:Eric's commits primarily focus on introducing new Kubernetes files and implementing changes related to the deployment of TFX pipelines on Kubernetes. These changes include creating and modifying container entrypoints and Kubernetes dag runner files. Further contributions involve configuring and integrating the TFX service account within the Kubernetes environment to ensure proper resource access and pipeline orchestration.
deployingend-to-endml-pipelinesmlmlops
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