The bulk of the interview was really about failure modes, not the happy path.
Start by clarifying the scope and requirements of the video generation system, then outline a high-level architecture covering data processing, model training, and inference serving. Dive into key components like distributed training, efficient inference, and storage, discussing trade-offs and scalability.
Pro tip: Emphasize the unique challenges of video generation, such as temporal coherence and high computational demands, and propose concrete solutions like 3D convolutions or transformer-based architectures with efficient attention mechanisms.
Ask questions to understand the expected scale, latency, quality, and use cases (e.g., text-to-video, video editing) to tailor the design.
Sketch the main components: data ingestion and preprocessing, model training pipeline, inference service, and storage for videos and metadata.
Discuss the model architecture (e.g., diffusion models, transformers), distributed training strategies, and inference optimization techniques like model parallelism and caching.
Explain how to scale training and inference using cloud resources, handle failures, and ensure low-latency serving with load balancing and autoscaling.
Highlight trade-offs between quality, speed, and cost, and mention potential improvements like better temporal modeling or efficient sampling methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.