Matthew Owen
Software Engineer at Clay
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Owen is a software engineer based in San Francisco with eight years of hands-on experience spanning distributed systems, backend data engineering, robotics, and computer security. A UC Berkeley EECS alumnus and former CS61B/CS61BL instructor, he blends production-grade engineering with teaching and mentorship—having built the first fully online version of the Data Structures course and managed large instructional teams. At Anyscale he made measurable improvements to Ray Data’s scalability, observability, and I/O performance (including download expression API and large-file chunking) and contributed core backend fixes to the widely used open-source Ray project. He has also shipped cloud-native data pipelines and remediation systems in healthcare and practical FPGA work at NASA, showing comfort across hardware and cloud stacks. Actively collaborating with product and observability teams, he brings a pragmatic focus on measurable performance gains and developer-facing tooling.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BS), Electrical Engineering and Computer Science, Bachelor of Science (BS), Electrical Engineering and Computer Science at UC Berkeley College of EngineeringGlenelg High School
English, Spanish
Github Skills (15)
data-loading10
logging10
data-pipeline10
data-pipelines10
pyarrow10
python10
metric10
data-engineering10
backend9
performance-optimization9
back-end-development9
distributed-systems8
datasets8
huggingface-datasets8
machine-learning7
Programming languages (5)
Github contributions (5)
ray-project/ray
Jan 2024 - Mar 2025
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:
Data Engineer & Backend Developer
Data Engineer & Backend DeveloperContributions:1 release, 205 reviews, 61 PRs in 1 year 2 months
Contributions summary:Matthew primarily contributed to the Ray Data project by improving data processing and I/O functionality. Their commits addressed data loading and reading issues related to formats like JSON and Hugging Face datasets, optimizing performance and handling edge cases. They also worked on enhancing metrics and logging to provide better insights into data processing pipelines, including metrics related to backpressure and task duration, and they worked on core backend functionality for Ray Data by making changes to the core libraries.
pythonconsistsruntimetensorflowserving
omatthew98/ray
Jan 2024 - Mar 2025
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Contributions:299 pushes, 54 branches in 1 year 2 months
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