Start by clarifying requirements and scale (e.g., number of users, videos per day, latency expectations). Then propose a high-level architecture with key components like API gateway, job queue, scheduler, workers, and storage, and discuss trade-offs for scaling each component. Finally, dive into data modeling and how you'd handle bottlenecks like GPU availability and cost.
Pro tip: Emphasize the unique challenges of video generation (long-running GPU tasks, large file storage) and how you'd design for fault tolerance and cost efficiency, showing you understand OpenAI's specific domain.
Ask questions to understand expected scale (e.g., daily active users, videos per day), latency requirements, and budget constraints. This ensures your design is appropriately sized.
Sketch the main components: client, API gateway, job queue, scheduler, worker pool (with GPUs), storage (for input/output), and database. Explain the flow from request to video delivery.
Discuss the scheduler's role in managing GPU resources, the queue for decoupling, and how workers process jobs. Mention trade-offs like synchronous vs. asynchronous processing.
Explain how to scale each component (e.g., horizontal scaling of workers, sharding the database) and ensure reliability (retries, dead-letter queues, monitoring).
Describe the data schema for jobs, users, and videos, and choose appropriate storage solutions (e.g., object storage for videos, relational DB for metadata).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.