How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others. In recent years, AutoML algorithms have emerged [e.g., 1, 2, 3] to help researchers find the right neural network automatically without the need for manual experimentation. Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space. With the proper setup, these techniques have demonstrated they are capable of delivering results that are better than the manually designed counterparts.