  The WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of
optical and near infrared instruments, including an extreme precision, optical
spectrograph, NEID, built for exoplanet radial velocity studies. In order to
achieve sub ms precision, NEID has strict requirements on survey efficiency,
stellar image positioning, and guiding performance, which have exceeded the
native capabilities of the telescope's original pointing and tracking system.
In order to improve the operational efficiency of the telescope we have
developed a novel telescope pointing system, built on a recurrent neural
network, that does not rely on the usual pointing models (TPoint or other quasi
physical bases). We discuss the development of this system, how the intrinsic
properties of the pointing problem inform our network design, and show
preliminary results from our best models. We also discuss plans for the
generalization of this framework, so that it can be applied at other sites.
