Particle-in-Cell (PIC) simulations are a popular approach to plasma physics problems in a variety of applications. These simulations range from interactive to very large, and are well suited to parallel architectures, such as GPUs. PIC simulations frequently serve as input to other simulations, as a part of a larger system. Our project has two goals: facilitate exploitation of increasing availability of parallel compute resources in PIC simulation, and provide an intuitive and efficient programmatic interface to these simulations. We plan to build a modular backend with multiple levels of parallelism using tools such as PyCUDA/PyOpenCL and IPython. The modular design, following the goals of our Object-Oriented Particle-in-Cell (OOPIC) code this is to replace, enables comparison of multiple algorithms and approaches. On the frontend, we will use a runtime compilation model to generate an optimized simulation based on available resources and input specification. Maintaining NumPy arrays as the fundamental data structure of diagnostics will allow users great flexibility for data analysis, allowing the use of many existing powerful tools for Python, as well as the definition of arbitrary derivative diagnostics in flight. The general design and preliminary performance results with the PyCUDA backend will be presented. This project is early in development, and input is welcome.