Google·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jul 2026

Summary

Did a system design round at Google for an MLE role. The question was around building a recommendation system and my feedback came back pretty lukewarm, so make of that what you will.

Questions Asked (1)

Design a recommendation system for products or videos.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

Felt okay in the moment but the feedback said otherwise.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope (products vs. videos, scale, business goals) and then walk through a high-level system design covering data, candidate generation, ranking, and serving. Emphasize trade-offs, offline/online evaluation, and A/B testing methodology, while highlighting ML-specific considerations like feature engineering, model selection, and feedback loops.

Pro tip: Demonstrate awareness of the cold-start problem and how to handle it with content-based or hybrid approaches, and discuss how to measure long-term user satisfaction beyond click-through rate to avoid optimizing for short-term engagement.

1. Clarify Requirements and Scope

Ask questions to understand the product type (e.g., e-commerce vs. video), scale (users, items), latency constraints, and business objectives (e.g., maximize watch time, revenue). Define success metrics and constraints.

2. High-Level Architecture

Outline the main components: data ingestion, feature store, candidate generation, ranking, and serving. Explain how they interact and the flow from user request to recommendations.

3. Candidate Generation and Ranking

Detail the two-stage approach: candidate generation (e.g., collaborative filtering, embeddings, ANN search) to narrow down millions of items to hundreds, then ranking (e.g., deep neural networks) to score and order them.

4. Model Training and Features

Discuss feature engineering (user, item, context), model choices (matrix factorization, two-tower, DNNs), training data (implicit/explicit feedback), and handling of cold-start and scalability.

5. Evaluation and Experimentation

Explain offline evaluation (precision@k, recall, NDCG) and online A/B testing. Cover how to design experiments, measure metrics, and iterate, including guardrail metrics and long-term effects.

Key Points to Mention

Two-stage architecture: candidate generation + ranking for scalability and relevance.
Feature engineering: user demographics, item metadata, contextual features, and embeddings.
Handling cold-start with content-based or hybrid methods and exploration strategies.
Offline metrics (e.g., NDCG, recall) and online A/B testing with proper experiment design.
Trade-offs: latency vs. accuracy, model complexity vs. interpretability, and short-term vs. long-term objectives.
Feedback loops and bias: how to avoid popularity bias and ensure diversity in recommendations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.