Analog-to-digital conversion allows physical signals to be processed using digital hardware. This conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. This conversion is typically carried out using generic uniform mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented analog-to-digital converters (ADCs) which operate in a data-driven manner, namely they learn how to map an analog signal into a sampled digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization which both faithfully represents these operations while allowing the system to learn non-uniform mappings from training data. We focus on the task of symbol detection in multipleinput multiple-output (MIMO) digital receivers, where multiple analog signals are simultaneously acquired in order to recover a set of discrete information symbols. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating without quantization constraints, while achieving more accurate digital representation compared to utilizing conventional uniform ADCs.