Amazon·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Amazon system design round, got asked to build a recommendation engine for a music streaming product. Pretty open-ended and I wasn't totally sure how deep to go on the ML side versus the infrastructure side.

Questions Asked (1)

Design a recommendation system for a music streaming platform similar to Spotify.

System DesignProduct Sense & IdeationTechnical Trade-offs

Author's notes

I started with the data layer and worked outward, which in hindsight was probably backwards.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then outline a high-level architecture covering data collection, candidate generation, ranking, and serving. Dive into trade-offs for key components like collaborative filtering vs. content-based, and discuss how to handle cold start and real-time updates.

Pro tip: Emphasize how you would measure success (e.g., CTR, session length) and iterate with A/B testing, showing a product-minded approach that Amazon values.

1. Clarify Requirements and Scale

Ask about user base size, song catalog, latency requirements, and personalization goals. Establish assumptions for DAU, QPS, and data volume.

2. High-Level Architecture

Outline main components: data ingestion (user interactions, song metadata), offline training pipeline, online serving layer, and feedback loop. Sketch a diagram.

3. Recommendation Algorithms

Discuss candidate generation (e.g., collaborative filtering, content-based, embeddings) and ranking (e.g., learning to rank, deep neural networks). Explain how to combine them.

4. Scalability and Real-Time Serving

Address how to handle low-latency serving, real-time updates, and scale using caching, sharding, and stream processing (e.g., Kafka, Flink).

5. Evaluation and Iteration

Define offline metrics (precision@k, recall) and online metrics (CTR, skip rate, session length). Describe A/B testing and continuous improvement.

Key Points to Mention

Cold start problem: how to recommend for new users and new songs using content-based or hybrid approaches.
Scalability: handling millions of users and songs with low latency using distributed systems and caching.
Real-time personalization: incorporating recent user actions via stream processing and online learning.
Trade-offs: collaborative filtering vs. content-based; batch vs. real-time; model complexity vs. latency.
Evaluation metrics: offline vs. online, and how to avoid feedback loops.
Diversity and freshness: ensuring recommendations are not too narrow and include new content.

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