A CUDA ConvNet developer leverages the CUDA programming model to design and implement convolutional neural networks (ConvNets), which are primarily used in deep learning algorithms for image and video processing. They optimize these neural networks for GPU processing to ensure efficient performance. Their responsibilities include writing, debugging, and optimizing CUDA code, managing GPU memory, and integrating ConvNets with other software components. They often work closely with data scientists to understand the requirements of the ConvNet and apply their knowledge of parallel computing and machine learning to deliver an optimized solution.