Whiteboard Confidential
REPLAY EPISODE: In this Google machine learning system design interview mock, a candidate tackles a personalized newsfeed recommendation system — the kind of large-scale ML challenge that real Google engineers face. 🧩 Problem: Design an ML system that ranks and recommends posts in a user’s feed by predicting engagement (likes, comments, shares) in real time. Watch how the candidate approaches it like a real interview: ✅ Clarifies goals, scope, and constraints for a production ML system ✅ Defines the ML objective and key features (user, content, interaction) ✅ Chooses and explains a two-tower deep learning architecture with multitask learning ✅ Discusses tradeoffs in retrieval, ranking, latency, and scalability 💡 What you’ll learn: • How to approach ML system design questions in Google interviews • How to connect engagement metrics to ML objectives • What a two-tower recommendation model looks like in production • How top candidates communicate complex ML ideas clearly 👉 Watch more interviews or book ML interview coaching: https://www.interviewing.io 📝 See the full transcript and interviewer feedback:https://interviewing.io/mocks/google-machine-learning-personalized-newsfeed-system 🔗 More Google interviews:https://interviewing.io/mocks?company=googleDisclaimer: All interviews are shared with explicit permission from both the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.
15 episoder
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