Picking horizons from seismic images is a fundamental step that could critically impact seismic interpretation quality. We have developed an unsupervised approach, waveform embedding, based on a deep convolutional autoencoder network to learn to transform seismic waveform samples to a latent space in which any waveform can be represented as an embedded vector. The regularizing mechanism of the autoencoder ensures that similar waveform patterns are mapped to embedded vectors with a shorter distance in the latent space. Within a search region, we transform all of the waveform samples to the latent space and compute their corresponding distance to the embedded vector of a control point that is set to the target horizon. We then convert the distance to a horizon probability map that highlights where the horizon is likely to be located. This method can guide horizon picking across lateral discontinuities such as faults, and it is insensitive to noise and lateral distortions. In addition, our unsupervised learning algorithm requires no training labels. We apply our horizon-picking method to multiple 2D/3D examples and obtain results more accurate than the baseline method.