Domain Expertise in Machine Learning Questions

Advanced understanding and practical knowledge in a candidate's primary machine learning subfield. For natural language processing this includes language model architectures, tokenization strategies, attention mechanisms, pretraining and fine tuning protocols, evaluation metrics, and common benchmark datasets. For computer vision this includes representation learning, object detection, segmentation, and robustness to distribution shift. For recommender systems this includes ranking algorithms, collaborative filtering, causal inference approaches, and online and offline evaluation methods. For junior candidates emphasize foundational concepts and key papers while senior candidates should be prepared to discuss open problems, systems integration, and scalability trade offs.