I spent the first few minutes rambling about user intent before I even touched the ML side, which was probably the wrong call for this role.
Start by clarifying the goal of the Explore page (discovery and engagement) and the ML engineer role's focus on ranking and personalization. Then outline a system design that covers data, models, and serving, while also discussing product strategy and metrics. Finally, tie it back to how ML drives business impact.
Pro tip: Emphasize the trade-off between exploration and exploitation, and how you'd measure long-term user satisfaction rather than just click-through rate. Show awareness of Meta's scale and infrastructure.
Ask questions to understand the page's purpose, target users, and success metrics. Define the scope: is it for content discovery, increasing engagement, or showcasing diverse content?
Outline the end-to-end ML pipeline: data collection, feature engineering, candidate generation, ranking, and serving. Mention the need for real-time and batch processing.
Describe the models: candidate generation (e.g., two-tower), ranking (e.g., deep learning with multi-task objectives), and exploration strategies (e.g., bandits). Discuss how to handle cold start and diversity.
Define offline and online metrics: AUC, NDCG for offline; CTR, dwell time, and long-term retention for online. Discuss A/B testing and counterfactual evaluation.
Explain how to balance competing objectives (e.g., relevance vs. diversity) and how to iterate based on user feedback. Mention potential challenges like filter bubbles and how to mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.