We study computational and data-driven methods that will augment or transform the activities of engineering and industrial design. Formally, we consider design activities the process of defining and solving optimization problems that are potentially high-dimensional, and seek search methods that leverage human knowledge and heuristics, learning algorithms, physics-based models, and computing hardware to achieve scalable design beyond the capacity of experienced human designers and existing algorithms. Challenges lie across multiple disciplines including optimization, machine learning, crowdsourcing, and engineering system design and control. Key real-world applications include computational material design and manufacturing, vehicle and robotics system design and control, and the automation of industrial design for functionalities and preferences.