Connor Doyle is a seasoned technology leader and founder with 14 years of experience building high-performance, cloud-native and AI-driven systems. As Co-Founder and CTO of Poggio Labs he combines deep distributed-systems and Kubernetes expertise with a product-first approach to enterprise sales AI, drawing on prior architecture work at Intel and early platform leadership at Mesosphere. He’s a hands-on engineer who has contributed to prominent open-source projects like Apache Mesos and Kubernetes node-feature-discovery, and who built critical automation and CI tooling for the Mesosphere Universe package repository. Connor’s background spans low-latency ML scheduling, container orchestration, and robust backend services, and he pairs that technical depth with repeated startup founding experience and enterprise customer engagement.
14 years of coding experience
9 years of employment as a software developer
University of San Francisco School of Management
Master of Software Engineering Software Engineering, Master of Software Engineering Software Engineering at University of Wisconsin-La Crosse
Deploy and manage containers (including Docker) on top of Apache Mesos at scale.
Role in this project:
Back-end Developer
Contributions:605 commits, 60 PRs, 74 pushes in 1 year 10 months
Contributions summary:Connor primarily contributed to implementing and enhancing the HTTP API within the Marathon project. Their work involved adding a new `/v1/apps/update` endpoint, implementing the `AppUpdate` class for modifying application parameters, and creating methods for updating applications within the `MarathonSchedulerService` and `MarathonScheduler` classes. Further contributions included fixing compilation errors and refactoring related classes to align with the evolving API.
Contributions:28 commits, 22 PRs, 12 pushes in 4 months
Contributions summary:Connor primarily focused on improving the scheduler and work manager components of the Snap framework. Their contributions include refactoring the queuedJob to use a promise abstraction, which simplifies asynchronous operations. Furthermore, the user enhanced the work manager by adding error handling for dropped jobs and implemented timeout mechanisms for promise operations, improving the robustness of the system. These changes collectively contribute to the framework's stability and management of tasks.
telemetryopen-telemetry
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