Design a Multimodal Neural Network
AmazonApplied Scientist · Onsite
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This question evaluates a candidate's competency in designing production-grade multimodal machine learning systems, including architecture choices for text and image encoders, cross-modal fusion strategies, training objectives for joint retrieval and classification, robustness to missing or noisy modalities, and considerations for scalability and low-latency serving. Commonly asked in ML system design interviews to assess both conceptual understanding and practical application of machine learning engineering principles, it falls under the ML System Design domain and probes abilities in model alignment, evaluation metrics, deployment trade-offs, and domain adaptation strategies.
Design a multimodal neural network that fuses text and images to perform retrieval and classification. Specify encoders for each modality, the fusion strategy (early fusion, late fusion, or cross-attention), training objectives (e.g., contrastive loss, joint classification), handling of missing or noisy modalities, data alignment/augmentation, and evaluation metrics. Discuss scalability, serving latency, and how you would fine-tune on new domains.
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