Title: Developing imaging technologies to search for, discover, and understand life in the deep sea Abstract: The deep sea is the largest habitable ecosystem on the planet and remains one of the least explored. Very little is known about deep sea inhabitants, their behavior, and the limits and drivers for their survival. Ecomechanics, the multidisciplinary research field of the mechanisms that underlie organismal interactions and survival within their environment, has proven largely successful in terrestrial fields and lab-based organismal systems, but has had limited applicability to deep sea animals. The reason for this deficit is largely due to the technological challenges to access this environment for study, and the typically short duration of any observations, precluding documentation of many critical behaviors. Here we present a number of developments from MBARI’s Bioinspiration Lab that can be used to quantify biodiversity, biomechanics, and behavior of animals in the deep sea. All powered by computer vision, these developments include: two 4000 m-rated, ROV-based imaging systems DeepPIV and EyeRIS used to quantify time-resolved particle fields and structures in 2D and 3D, a 1500 m-rated AUV-based plenoptic imaging system Chiton, an underwater labeled image database called FathomNet that can be used to fuel machine learning algorithm development, and machine learning-integrated vehicle control algorithms called ML-Tracking that enable long-duration observations of individual animals. These instruments and approaches can be applied to a wide range of science use cases, both in the water column and on the seafloor.