In recent years, the convergence of marine science and computer vision has opened new avenues for advancing ecological research through automation, large-scale data analysis, and novel insights into marine systems. From habitat mapping using remotely operated vehicles to species identification in vast image datasets, computer vision has become a powerful tool for addressing complex marine challenges. At the same time, these applications often push the boundaries of standard computer vision workflows, requiring adaptations to the unique conditions and questions inherent to marine environments. This paper stems from years of interdisciplinary collaboration between marine scientists and computer vision researchers who have worked together at the intersection of these two domains. Rather than focusing on single studies or innovations, we take a step back to reflect on the collaborative processes that underpin successful projects and document how interdisciplinary teams worked together to achieve shared goals. First, we share collective lessons learned from real-world projects that required sustained communication across disciplinary boundaries. Second, we identify practical guidelines for others entering similar collaborations, grounded in both technical and interpersonal considerations. Finally, we present a blueprint for structuring and sustaining effective partnerships between marine ecologists and computer vision scientists, offering a process-focused perspective that complements existing methodological literature. By bringing together perspectives from both fields, this paper seeks to contribute to the growing discourse on interdisciplinary research practices in environmental informatics and applied AI. We hope it will serve as a valuable resource for researchers navigating similar partnerships, as well as for institutions and funders seeking to support such work better.