Intelligent systems are gaining in popularity and receiving increased media attention, but little is known about how people actually go about developing them. In this paper, we attempt to fill this gap through a set of field interviews that investigate how people develop intelligent systems that incorporate machine learning algorithms. The developers we interviewed were experienced at working with machine learning algorithms and dealing with the large amounts of data needed to develop intelligent systems. Despite their level of experience, we learned that they struggle to establish a repeatable process. They described problems with each step of the processes they perform, as well as cross-cutting issues that pervade multiple steps of their processes. The unique difficulties that developers like these face seem to point to a need for software engineering advances that address such machine learning systems, and we conclude by discussing this need and some of its implications.