Google·Machine Learning Engineer·Onsite - System Design / Architecture
- Design a recommendation system that, at each interaction, selects one item from four candidates to show a user, collects immediate feedback, and continuously updates the model to improve future selections. Walk through your model choice, feature engineering, how you handle feedback, your exploration vs. exploitation strategy, and how you'd evaluate the system both offline and online.
“I went with a contextual bandit setup using a linear model with upper confidence bounds, which felt safe but I second-guessed myself mid-answer and started rambling about neural bandits before snapping back.”