Risk assessment for large engineered systems involving geotechnical uncertainties—such as dams, coastal works, industrial facilities, and offshore structures—has matured in recent decades. This capability is becoming an ever more pressing concern considering increasing natural hazards and the effects of climate change. This progress and the current state of practice are reviewed by considering four projects. An underlying theme is what we have learned about how geoprofessionals should think about uncertainty, judgment, and risk. The organizing key is Bayesian thinking, which is neither new nor unintuitive to most geoprofessionals. However, this way of thinking and analyzing data is powerful, even when dealing with the sparse data of geotechnical projects. The presentation builds on the past Terzaghi Lectures of Casagrande, Whitman, and Christian. It uses examples of infrastructure systems for which geotechnical considerations were important. Reliability concepts developed in this earlier practice provide a window into the processes of integrating site characterization data, judgment, engineering models, and predictions to achieve tolerable levels of risk. Along the way, arguments are made on the nature of uncertainty and on the benefits of processing data, judgment, and inferences through a consistent lens.