The real world is a 280 dB High Dynamic Range (HDR) world which imaging sensors cannot record in a single shot. HDR cameras acquire multiple measurements with different exposures, gains and photodiodes, from which an Image Signal Processor (ISP) reconstructs an HDR image. Dynamic scene HDR image recovery is an open challenge because of motion and because stitched captures have different noise characteristics, resulting in artifacts that ISPs must resolve in real time at double-digit megapixel resolutions. Traditionally, ISP settings used by downstream vision modules are chosen by domain experts; such frozen camera designs are then used for training data acquisition and supervised learning of downstream vision modules. We depart from this paradigm and formulate HDR ISP hyperparameter search as an end-to-end optimization problem, proposing a mixed 0th and 1st-order block coordinate descent optimizer that jointly learns sensor, ISP and detector network weights using RAW image data augmented with emulated SNR transition region artifacts. We assess the proposed method for human vision and image understanding. For automotive object detection, the method improves mAP and mAR by 33% over expert-tuning and 22% over state-of-the-art optimization methods, outperforming expert-tuned HDR imaging and vision pipelines in all HDR laboratory rig and field experiments.