Generalized linear mixed-effects models are widely used for grouped and hierarchical data, but their linear structure can be limiting. We replace the linear predictor with neural networks, yielding neural generalized mixed-effects models, and introduce a differentiable approximation to the marginal likelihood for efficient optimization.