Parallel applications typically run in batch mode, sometimes after long waits in a scheduler queue. In some situations, it would be desirable to interactively add new functionality to the running application, without having to recompile and rerun it. For example, a debugger could upload code to perform consistency checks, or a data analyst could upload code to perform new statistical tests. This paper presents a scalable technique to dynamically insert code into running parallel applications. We describe and evaluate an implementation of this idea that allows a user to upload Python code into running parallel applications. This uploaded code will run in concert with the main code. We prove the effectiveness of this technique in two case studies: parallel debugging to support introspection and data analysis of large cosmological datasets.