Three Onsite Rounds on Multimodal Deployment and Dropout

Round 1 Discussed how to deploy a multimodal model when compute and GPU memory are limited. Follow-up: given existing captions and embeddings, how to speed up video retrieval. Asked what overfitting is and how to mitigate it. Coding question: implement a MinStack that returns the minimum in O(1) time. Round 2 Discussed methods to mitigate overfitting in deep learning and how Dropout works. Compar…

Shared by a candidate · reported Jan 2026. Interview formats change; use it as a guide, not a script.