Ray, an AI compute platform, has introduced label selectors to enhance scheduling flexibility by allowing developers to specify where workloads should run based on node characteristics. Released in Ray 2.49, this feature is available across Ray Dashboard, KubeRay, and Anyscale, enabling users to assign labels to nodes, such as CPU type or market type (spot or on-demand), and use these labels to direct task and resource placement more efficiently. The new API addresses prior limitations by providing a more intuitive way to express scheduling requirements without resorting to hacks, improving developer experience and debugging. Inspired by Kubernetes labels and selectors, the integration fosters interoperability between Ray and Kubernetes, potentially unlocking advanced use cases. The system supports both static and autoscaling clusters, with future plans to extend label selector functionalities to Ray libraries and improve Kubernetes interoperability further.