Nathan Liu
Principal Scientist - Amazon Ads
Saratoga, California, United States
Summary
🤩
Rockstar🎓
Top SchoolNathan Liu is a Principal Scientist at Amazon Ads with six years of focused industry experience building production-grade machine learning systems for search, recommendation, personalization, and advertising. He combines a PhD-level research background from HKUST with senior roles across YouTube, Yahoo, LinkedIn and startups, delivering cloud-native ML solutions and campaign optimization products. Nathan has deep expertise in recommender systems, NLP, time series analysis and Python, and has contributed to the well-known Apache Gobblin project by improving Kafka integration and robust container resource handling—showing an attention to scalable data integration beyond model development. Based in Saratoga, California, he blends research rigor with pragmatic engineering, often stepping into backend platform work to ensure models operate reliably at scale.
6 years of coding experience
10 years of employment as a software developer
The Chinese University of Hong Kong (CUHK)Beijing No.4 High SchoolHong Kong University of Science and Technology (HKUST)
Github Skills (8)
javas10
kafka10
data-integration10
java10
testing8
testng8
apache7
json7
Programming languages (1)
Github contributions (5)
apache/gobblin
Dec 2020 - Oct 2022
A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:81 reviews, 12 commits, 26 PRs in 1 year 10 months
Contributions summary:Nathan primarily focused on enhancing Apache Gobblin's Kafka integration. They added support for Kafka 1.x, developed features for metadata change events (GMCE), and improved the handling of container resources within the Yarn service. Furthermore, the user addressed JVM hangs during Kafka flush operations and optimized the container allocation process, demonstrating a strong understanding of the project's core data integration functionalities. They also contributed to test cases, ensuring the stability of these enhancements.
datadcosdata-streambig-data-integrationbatch-data
hanghangliu/gobblin
May 2020 - Jan 2024
Gobblin is a distributed big data integration framework (ingestion, replication, compliance, retention) for batch and streaming systems. Gobblin features integrations with Apache Hadoop, Apache Kafka, Salesforce, S3, MySQL, Google etc.
Contributions:97 pushes, 20 branches in 3 years 8 months
salesforceretentionbig-data-integrationcompliancekafka-connect
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.