Ultra-low-field (uLF) MRI systems offer portable and affordable neuroimaging solutions for pediatric patients and are valuable in resource-limited settings. However, such systems are susceptible to poor image quality, artifacts, and low contrast, making brain segmentation difficult. This study addresses two critical challenges in uLF MRI: automated quality assessment (QA) and anatomical structure segmentation. For QA, we propose a multi-label approach that incorporates the ordinal nature of artifact severity through an ordinal loss and models artifact co-occurrence patterns using Bayesian Networks. The approach is enhanced through aggressive synthetic data augmentation and ensemble learning, achieving a composite accuracy score of 0.84 across seven artifact categories. For segmentation, we benchmark a task-specific model (nnU-net) against a foundation model (SAM-Med3D) on the delineation of challenging subcortical structures. While nnU-Net, trained from scratch, achieved mean Dice score of 0.72 for hippocampi and 0.86 for basal ganglia, we demonstrate that lightweight fine-tuning of SAM-Med3D yields comparable results with a mean Dice score of 0.70 in hippocampi segmentation, despite domain shift. These results underscore the promise of foundation models for medical imaging in low-resource contexts, while highlighting the importance of domain adaptation. Overall, our pipeline represents a step forward in robust, automated QA and segmentation in uLF MRI for pediatric use. We release the code at https://github.com/reitxel/LISA2025TeamUPF .