Jason Dai
Software Engineer, Machine Learning GenAI at Google
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Dai is a software engineer specializing in machine learning and generative AI, currently building evaluation services for Vertex Generative AI at Google. With a BS and MS in Electrical Engineering from Columbia (Magna Cum Laude) and six years of industry experience spanning Qualcomm, Nokia Bell Labs, and academic research, he blends deep-learning research with production-grade cloud and wireless systems. He contributed to Kubeflow Pipelines, adding Google Cloud components for model evaluation and data splitting—bridging experimental ML workflows and scalable deployment. Based in Sunnyvale, he brings a research-driven mindset to practical ML engineering, informed by presentations at Bell Labs, MIT, and Columbia. Notably, his background in advanced wireless SoC work gives him a cross-domain perspective useful for latency- and resource-sensitive AI systems.
6 years of coding experience
4 years of employment as a software developer
Mountain Brook High SchoolMaster of Science - MS Electrical Engineering, Master of Science - MS Electrical Engineering at Columbia UniversityBirmingham-Southern College
Chinese, English, Spanish
Github Skills (14)
kubeflow10
machine-learning10
google-cloud-platform10
data-pipeline10
data-pipelines10
python10
pipe10
pipeline10
gcp10
data-science9
yaml9
mlops9
kubernetes8
kubernetes-pods8
Programming languages (4)
Github contributions (5)
kubeflow/pipelines
Jun 2022 - Jan 2023
Machine Learning Pipelines for Kubeflow
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 15 commits in 7 months
Contributions summary:Jason primarily contributed to the development of machine learning pipelines within the Kubeflow framework. Their commits added new components related to model evaluation, specifically focusing on data sampling and splitting for Google Cloud components. The user also created YAML definitions for new components, enhancing the functionality of the Kubeflow Pipelines for machine learning model evaluation workflows. Their work is centered on the experimental Google Cloud pipeline components.
pipelinetektondata-sciencemachine-learningmlops
jsondai/generative-ai
Apr 2024 - Mar 2025
Sample code and notebooks for Generative AI on Google Cloud
Contributions:69 pushes, 19 branches in 11 months
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