Wasn't expecting to finish early but we burned through all the follow-ups and the interviewer just kind of shrugged and said there was nothing left to dig into.
Start by clarifying the scope and requirements of the video generation system, such as resolution, duration, and quality. Then, outline a high-level architecture that includes data processing, model training, inference, and serving, focusing on scalability and trade-offs. Finally, dive into key components like the diffusion model, distributed training, and inference optimization, while addressing challenges like latency and cost.
Pro tip: Emphasize the importance of data quality and diversity, and discuss how you would handle the computational challenges of training and serving such a model at scale, showing awareness of OpenAI's infrastructure.
Ask questions to understand the expected video length, resolution, quality, and any constraints like latency or cost. This shows you can handle ambiguity and scope the problem effectively.
Propose a modular architecture: data pipeline for collecting and preprocessing videos, a training pipeline for the generative model, and an inference service for generating videos. Mention the use of distributed systems and cloud infrastructure.
Discuss the choice of generative model, such as diffusion models or transformers, and how to handle temporal coherence. Explain how you would train it, including data requirements and compute resources.
Address how to scale training and inference, using techniques like model parallelism, mixed precision, and caching. Discuss trade-offs between quality, speed, and cost.
Describe metrics for evaluating generated videos (e.g., FVD, human evaluation) and how to iterate on the model and system based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.