Summary
Jieun Jeon is a Machine Learning Engineer with nine years of full-stack software experience, now focused on productionizing ML platforms and improving infrastructure performance. She has led architecture and implementation of cloud-native ML pipelines—building logging/alerting, CI/CD, and Kubernetes/Kubeflow deployments—across finance, healthcare genomics, and automotive imaging projects. Her background spans end-to-end product delivery from backend APIs and ETL pipelines to responsive web/mobile UIs, having shipped systems that process large daily datasets and cut search latency by 10x. Based in California and trained at the University of Wisconsin–Madison (BS Computer Science, 3.7), she combines hands-on engineering with a data-centered, human-in-the-loop approach to improve model reliability and user experience. Notably, she has practical experience converting raw genomic and image data into production analytics through scalable AWS and GCP architectures.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BS), Computer Science, 3.7 (out of 4.0), Bachelor of Science (BS), Computer Science, 3.7 (out of 4.0) at University of Wisconsin-Madison
English, Korean