Joseph Burnett is a Principal Engineer with 13 years of experience building and scaling distributed systems, currently shaping deployment and administrative APIs for App Engine at Google Cloud and now driving backend strategy at GitLab. A former Marine, he blends operational discipline with deep technical expertise in autoscaling, Kubernetes, and serverless (notably contributions to Knative and Kubernetes HPA). He’s a hands-on engineer who’s implemented core diff/patch logic in open-source tooling and worked across the stack—from sonic-pi’s live web GUI to high-impact autoscaler improvements for multi-tenant environments. Known for improving reliability and scale-to-zero behaviors, he pairs rigorous testing and automation with pragmatic design. Based in Wenatchee, WA, he brings a proven track record of moving cloud-native features from prototype to production at major platforms.
13 years of coding experience
15 years of employment as a software developer
BA Computer Science, BA Computer Science at Tufts University
AA Political Science, AA Political Science at Highline College
Contributions:24 releases, 7 reviews, 309 commits in 6 years 2 months
Contributions summary:Joseph primarily focused on implementing core functionalities for the JSON diff and patch tool, creating the `jsonNode.equals` method for comparing nodes, and implementing the `diff` method. They added types for various JSON structures like `JsonString`, `JsonList`, `JsonStruct` and `JsonNumber`. They also developed tests for verifying equality and difference between JSON objects and performed unit tests and integration tests for JSON Patch (RFC 6902) using different data formats. They implemented `RenderPatch` and `RenderMerge` for generating various diff output formats.
Contributions:58 commits, 45 PRs, 12 pushes in 1 year 4 months
Contributions summary:Joseph's commits primarily focused on the development and enhancement of the autoscaler component within the Knative serving project. They implemented features related to autoscaling behavior, including setting CPU targets, handling concurrency, and integrating DNS resolution. The user also contributed to the reliability of the autoscaler and improved the performance metrics, including the transition to a multi-tenant autoscaler. Furthermore, the user addressed scaling down to zero and refactored the activator code.
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