Edward Oakes
Staff Software Engineer at Anyscale
Minneapolis, Minnesota, United States
Summary
🤩
Rockstar🎓
Top SchoolEdward Oakes is a Staff Software Engineer with 11 years of experience building and hardening large-scale distributed systems, currently contributing to Anyscale and the Ray AI compute engine. He focuses on backend and DevOps work—improving runtime environments, container reliability, and production infrastructure—bringing research-grade rigor from his MS at UC Berkeley. Edward has practical ML production experience from comScore and has improved developer experience through tutorials and Colab exercises for Ray. Colleagues describe him as detail-oriented—he "cares about the little things"—which shows up in thoughtful cleanup, failure handling, and build tooling across open-source projects like Ray and Open Lambda. Based in Minneapolis, he blends academic research, startup internships, and sustained open-source impact to make distributed AI systems more reliable and user-friendly.
11 years of coding experience
5 years of employment as a software developer
Master of Science - MS Electrical Engineering and Computer Science, Master of Science - MS Electrical Engineering and Computer Science at University of California, BerkeleyBachelor of Science (BS) Computer Science, Bachelor of Science (BS) Computer Science at University of Wisconsin-Madison
German, English
Stackoverflow
21reputation
597reached
1answer
1question
Github Skills (22)
docker10
develop10
python10
distributed-systems10
containerization10
dockers10
google-colab10
go10
ray10
github9
devops9
serverless9
microservices8
microservices-application8
build-system8
Programming languages (6)
Github contributions (5)
open-lambda/open-lambda
May 2016 - Oct 2018
An open source serverless computing platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:332 commits, 2 PRs, 238 pushes in 2 years 4 months
Contributions summary:Edward primarily contributed to enhancing the Open Lambda serverless computing platform, specifically focusing on improving the management and functionality of the underlying Docker containers and other infrastructure components. Their commits demonstrate a focus on improving the reliability of the container environment by correctly handling the container state and dealing with the proper cleanup of resources. Further, the user added support for features such as relative paths and environment files, which directly improved the flexibility of the build process and support for the platform.
golang-applicationserverlessdistributed-systemsmicroservicescomputing
ray-project/ray
Jul 2019 - Aug 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 Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:4 releases, 4421 reviews, 599 commits in 3 years 1 month
Contributions summary:Edward's commits primarily focus on improving the Ray runtime environment, including gracefully handling task failures when environments fail and setting up runtime environments for client server connections. The changes touch upon various backend systems of the Ray project, indicating a strong understanding of the project's internals. Additionally, the user's modifications to the build, including skipping tests, and environment configuration suggests some DevOps experience.
pythonconsistsruntimetensorflowserving
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