Meta·Software Engineer·Technical Phone Screen·Senior

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Jul 2026

Summary

Meta system design screen, one question about building type-ahead search for Netflix. Pretty lean on details but the core problem is meaty enough to chew on for a while.

Questions Asked (1)

Design a type-ahead search system for a streaming platform like Netflix.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates the suggestion service from the core search infrastructure. Focus on low-latency prefix matching, ranking strategies, and trade-offs between freshness and performance.

Pro tip: Emphasize the importance of measuring and optimizing the p99 latency for keystroke-level queries, and discuss how to handle popular queries during traffic spikes with caching and precomputation.

1. Clarify Requirements

Ask about scale (DAU, QPS), latency targets, data sources (titles, people, genres), and personalization needs. Confirm whether suggestions should be based on popularity, user history, or both.

2. High-Level Design

Outline components: client, API gateway, suggestion service, data stores (trie, inverted index, cache), and offline pipelines for indexing and ranking. Explain data flow from user input to suggestions.

3. Data Modeling & Indexing

Describe how to build and maintain a prefix index (e.g., trie or finite state transducer) and how to incorporate metadata like popularity and personalization signals. Discuss batch vs. real-time updates.

4. Ranking & Personalization

Explain ranking strategies: blend popularity, recency, and user-specific signals. Discuss how to incorporate personalization without adding excessive latency, e.g., via precomputed user profiles.

5. Scaling & Trade-offs

Address scaling: sharding, replication, caching, and CDN usage. Discuss trade-offs between latency, freshness, and cost. Mention monitoring and A/B testing for quality.

Key Points to Mention

Low-latency requirements (p99 < 100ms) and techniques to achieve them (caching, in-memory indexes, edge computing).
Data structures for prefix matching: trie, ternary search tree, or finite state transducer, and their trade-offs.
Ranking signals: popularity, trending, user history, and contextual factors; how to combine them.
Personalization strategies: precomputed user embeddings or collaborative filtering, and how to serve them quickly.
Handling updates: batch indexing vs. real-time updates, and consistency trade-offs.
Monitoring and evaluation: metrics like CTR, latency, and A/B testing for continuous improvement.

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