Hunter Gregory is a Software Engineer III with 7 years building cloud-native networking and observability platforms, currently working on the API control plane for Google Compute Engine. Previously at Microsoft he designed and launched paid networking features for AKS, ran the control plane/operator work, and shipped a million-dollar network observability product that served hundreds of thousands of clusters and over a million VMs. He created and open-sourced Retina (3K stars) and Policy Assistant, led metric-driven design with Prometheus/Grafana integrations, and improved large-scale fault tolerance and performance across Linux and Windows datapaths. His Duke background in statistics and CS with an AI/ML concentration informs a data-first approach to monitoring, testing, and system design. Hunter combines hands-on backend and DevOps work with product-minded UX and operational readiness, and has a track record of turning complex network telemetry into actionable customer insights. An often-overlooked strength is his experience shipping both low-level performance optimizations and developer-facing tooling (CLIs and dashboards) that bridge engineering and operator workflows.
7 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Statistics & Computer Science with AI/ML concentration, Bachelor of Science - BS Statistics & Computer Science with AI/ML concentration at Duke University
Azure Container Networking Solutions for Linux and Windows Containers
Role in this project:
Back-end & DevOps Engineer
Contributions:554 reviews, 187 commits, 254 PRs in 2 years 6 months
Contributions summary:Hunter was primarily involved in enhancing the project's Prometheus metrics, incorporating features to monitor and expose critical data. They implemented metrics related to network policies, iptables rules, and IP sets, adding functionality for summary counts and summary values. The user also improved the test suite, including the addition of tests for the newly introduced Prometheus metrics and improved testing efficiency by refactoring existing code. Furthermore, they made several performance optimizations.
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