In-ice radio detection of neutrinos is a rapidly growing field and a promising technique for discovering the predicted but yet unobserved ultra-high-energy astrophysical neutrino flux. With the ongoing construction of the Radio Neutrino Observatory in Greenland (RNO-G) and the planned radio extension of IceCube-Gen2, we have a unique opportunity to improve the detector design now and accelerate the experimental outcome in the field for the coming decades. In this contribution, we present the first end-to-end in-ice radio neutrino simulation, detection, and reconstruction pipeline using generative machine learning models and differentiable programming. We demonstrate how this framework can be used to optimize the antenna layout of detectors to achieve the best possible reconstruction resolution of neutrino parameters.