Reconfigurable computing devices offer substantial improvements in functional density and yield versus traditional microprocessors, yet remain out of general-purpose use due in part to their difficulty of programming and lack of cross-device compatibility. In a stream-based compute model called SCORE (Stream Computations Organized for Reconfigurable Execution) was introduced with a goal to provide a programming model for easily exploiting the computational density of reconfigurable devices. SCORE virtualizes reconfigurable resources (compute, storage, and communication) by dividing a computation up into fixed-size "pages" and time-multiplexing the virtual pages on available physical hardware. Consequently, SCORE applications can scale up or down automatically to efficiently run on a wide range of hardware. In this project we implemented project implements a dynamic runtime scheduler for SCORE that virtualizes the reconfigurable computation fabric and automatically manages the execution of SCORE applications in hardware. Initial performance scaling experiments show that a dynamic scheduler is able to automatically scale applications on reduced hardware and exploit hardware under-utilization to achieve reasonable area-time curves. In this paper, we present the basic scheduler details and runtime system flow along with key implementation highlights, such as scheduling heuristics, memory management, and deadlock detection.