This is where I probably underinvested in the infrastructure side.
Start by clarifying the business goals and constraints, then outline a high-level architecture that separates candidate generation, ranking, and personalization. Dive into the ML components, data flow, and trade-offs, emphasizing scalability and real-time updates.
Pro tip: Show awareness of Shopify's multi-tenant architecture and the need for merchant-specific customization; discuss how you'd handle cold-start for new merchants and products without overcomplicating the system.
Ask about scale (number of merchants, products, users), latency requirements, and business objectives (e.g., increase conversion, discovery).
Sketch the system: data ingestion, feature store, candidate generation, ranking model, and serving layer. Mention offline vs online components.
Describe candidate generation (e.g., collaborative filtering, embeddings) and ranking (e.g., gradient boosted trees, neural networks). Discuss features and training.
Explain how to incorporate user behavior in real-time (e.g., session-based features) and handle cold-start with content-based or trending items.
Define offline metrics (e.g., NDCG, recall) and online A/B testing. Discuss monitoring, feedback loops, and retraining cadence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.