Explain CLIP, contrastive losses, and retrieval limits
Company: Snapchat
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Answer the following ML questions in the context of multi-modal (text–video/image) retrieval:
1) How does a **CLIP-style model** work conceptually (architecture, training signal, inference usage)?
2) What are common **contrastive learning loss functions** used for representation learning? Explain at least a few and when they are appropriate.
3) What are the main **disadvantages of embedding-based retrieval** (bi-encoder / vector search)?
4) What alternative approaches exist (e.g., cross-encoders, hybrid sparse+dense, generative retrieval), and what trade-offs do they make?
5) How would you handle or mitigate **popularity bias** in an embedding-based retrieval system?
Quick Answer: This question evaluates understanding of multi-modal representation learning and retrieval systems, covering CLIP-style joint image–text encoders, contrastive loss families, embedding-based retrieval limitations, alternative retrieval paradigms, and issues like popularity bias.