In this paper we present the design and experimental characterization of a tactile sensor for underwater manipulation. Water turbidity in energetic underwater environments can degrade the performance of perception sensors, making the execution of already difficult manipulation tasks even more challenging. Tactile sensing can provide useful information in these environments. One popular type of tactile sensor for terrestrial applications uses barometric pressure sensors encased in a soft elastomer. However, the performance of these sensors in changing ambient pressures has not been investigated. We designed a custom testbed to characterize high-pressure MEMS barometers embedded in two types of silicone up to 50 PSIG ambient pressure. Using characterization results from a single barometer, we then designed two 2 × 4 tactile grids. Datasets of differential pressures (against a control sensor) for varying contact locations were used to train feedforward neural networks for point load estimation. Results show that for the grid encased in softer silicone, the model performance improved as the ambient pressure increased (average RMSE of 0.33 mm).