  The planned in-ice radio array of IceCube-Gen2 at the South Pole will provide
unprecedented sensitivity to ultra-high-energy (UHE) neutrinos in the EeV
range. The ability of the detector to measure the neutrino's energy and
direction is of crucial importance. This contribution presents an end-to-end
reconstruction of both of these quantities for both detector components of the
hybrid radio array ('shallow' and 'deep') using deep neural networks (DNNs). We
are able to predict the neutrino's direction and energy precisely for all event
topologies, including the electron neutrino charged-current interactions, which
are more complex due to the LPM effect. This highlights the advantages of DNNs
for modeling the complex correlations in radio detector data, thereby enabling
a measurement of the neutrino energy and direction. We discuss how we can use
normalizing flows to predict the PDF for each individual event which allows
modeling the complex non-Gaussian uncertainty contours of the reconstructed
neutrino direction. Finally, we discuss how this work can be used to further
optimize the detector layout to improve its reconstruction performance.
