Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results toward unified and effective software engineering and experiment management software.