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.