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In 2021 GW Engineering was awarded funding to launch an interdisciplinary program on trustworthy AI. Designing Trustworthy AI in Systems (or DTAIS) brings together PhD students from systems engineering and computer science to co-design research and tackle the conceptual and methodological bridge building that cross disciplinary work demands. This paper focuses on how this work has been accomplished thus far, in the context of the cornerstone summer incubator, and shares some of the lessons learned. The 10-week summer incubator course, which was designed specifically for this program, brings systems engineers and computer science PhD students to make sense of “AI in the wild” (real world settings) and build short-run research prototypes together. Leveraging the interdisciplinarity of the core program faculty, the group established a fertile middle ground where a mixed method ethos, design sprint rhythm and intentional sense of community enlivens the normative student-advisor modality most PhD students experience. Along the way, the definitional challenge of what is meant exactly by trust and trustworthiness within a particular problem domain and literature is given plenty of room to form, fall apart and form again through discussion, practice, and reflection. With two iterations of the summer incubator course to glean from, we report on the difficulties of rewiring student-advisor dynamics and the positive effects of growing a diverse community. This represents a potential roadmap for how to scaffold interdisciplinarity in engineering doctoral education.