What are some common challenges faced by machine learning engineers working on gradient-based optimization techniques?

Machine learning engineers focusing on gradient-based optimization often encounter challenges such as vanishing or exploding gradients, which can hinder model training, especially in deep neural networks. Debugging issues related to convergence, learning rate selection, and ensuring numerical stability are key aspects of the role. Collaboration with data scientists and researchers is essential to refine models and experiment with different optimization strategies. Staying updated with the latest advancements in algorithms and techniques also helps address these challenges effectively.