Senior interview questionAsked at Pinterest · interview write-up
Design a multimodal system that detects unsafe user-generated content in real time, with policy-driven actions and human review.
Model answer
Cheapest first. Hash matching against known violating media catches repeats instantly. Then fast classifiers per policy category over fused image and text embeddings, including OCR text, score uploads; heavier models run asynchronously on video frames and borderline items.
A policy layer, not the model, maps scores to actions per category: block, limit distribution, blur, or queue. Thresholds come from precision and recall at each action. The uncertain band goes to human review, prioritized by severity times expected reach; reviewer decisions and appeals become training labels. Track prevalence, precision at each action, and time to action.
Practise the rest
12 Multimodal questions, 492 in all, each with a model answer. Free.
More from Pinterest interview questions.
More Multimodal interview questions
- What has driven each generation of LLM improvements, how do multimodal LLMs work, and what role does normalisation play in LLMs?
- You need to deploy multimodal models under tight GPU and latency limits, and also support fast retrieval over video. How do you approach it?
- How does a vision encoder feed images into a multimodal LLM, and where are the bottlenecks?
- Design the training of a multimodal embedding model: architecture, supervision and losses, evaluation metrics and deployment.
- Turn a multimodal model that captions videos into a production service under tight compute and GPU memory limits.
- Design a classifier that assigns genres to games using text, images and tabular metadata, from label quality to serving and monitoring.