Optimizing the efficiency of neural networks is cru-cial for ubiquitous machine learning on the edge. However, it requires specialized expertise to account for the wide variety of applications, edge devices, and deployment scenarios. An attractive approach to mitigate this bottleneck is Neural Architecture Search (NAS), as it allows for optimizing networks for both efficiency and task performance. This work shows that including hyperparameter optimization for training-related parameters alongside NAS enables substantial improvements in efficiency and task performance on a predictive maintenance task. Furthermore, this work extends the combination of NAS and hyperparameter optimization with INT8 quantization to enhance efficiency further. Our combined approach, which we refer to as Quantization-Aware NAS (QA-NAS), allows for further improvements in efficiency on the predictive maintenance task. Consequently, our work shows that QA-NAS is a promising research direction for optimizing neural networks for deployment on resource-constrained edge devices in industrial applications.