Patrick Ames
Distinguished Engineer - GEAR Lab at NVIDIA
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolPatrick Ames is a Distinguished Engineer specializing in data infrastructure and distributed systems, currently leading multimodal lakehouse and robotics research efforts at NVIDIA's GEAR Lab. He has driven exabyte-scale migrations and lakehouse format unification at Amazon, cofounding Ray DeltaCAT and contributing performance and autoscaling improvements to the prominent Ray project. His work blends systems-level engineering—column-oriented databases, high-throughput parquet/read optimization, and cloud autoscaling—with DevOps and security-hardened production deployments. Patrick consistently converts research into production: at Amazon he helped migrate a 50PB+ Oracle warehouse to an S3 lakehouse serving hundreds of thousands of datasets and millions of weekly jobs. He pairs deep C++ and distributed-systems chops with practical automation experience across AWS networking and launch templates, and has a background in real-time graphics and bioinformatics that informs creative problem solving. Based in San Francisco, he focuses on computational efficiency and practical open-source contributions that reduce cost and latency at scale.
6 years of coding experience
6 years of employment as a software developer
BS Computer Science, BS Computer Science at Brigham Young University
English, Korean
Github Skills (12)
ec10
parquet10
autoscaling10
ray10
amazon-ec210
aws10
devops10
python10
data-engineering9
deploying8
data-science8
distribute8
Programming languages (3)
Github contributions (5)
ray-project/ray
May 2020 - May 2022
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:93 reviews, 16 commits, 14 PRs in 1 year 11 months
Contributions summary:Patrick primarily worked on the autoscaler module, modifying configurations and fixing bugs related to AWS subnet IDs and security groups. They added support for custom EC2 network interfaces and launch templates, suggesting an emphasis on infrastructure automation and cloud resource management. Furthermore, they contributed to the dataset module, enhancing functionality around directory creation and arrow stream arguments. They also implemented a performance improvement on the parquet reader.
pythonconsistsruntimetensorflowserving
pdames/deltacat
Jan 2022 - Mar 2025
A Pythonic Data Catalog powered by Ray that brings exabyte-level scalability and fast, ACID-compliant, change-data-capture to your big data workloads.
Contributions:3 reviews, 117 pushes, 11 branches in 3 years 2 months
pythondata-catalogdata-integritydata-collectionbig-data-analytics
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