The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and coping with increasing complexity. A key challenge is automating the process of finding optimal model architectures satisfying stringent requirements stemming from varying tasks, available data, resources in the infrastructure, and deployment positions. In this paper, we look into using Neural Architecture Search (NAS) to automatically find the best model architecture for different telecom use cases. We discuss the main challenges for using NAS in telecom from the perspectives of tabular data, limited data availability, and multi-objective search. We propose a modification to existing NAS methods designed for tabular data to adapt them for multi-objective search and evaluate their performance in different network management and performance modeling scenarios, including scenarios where limited data is available during NAS.