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

SeniorPrefer not to say

Jul 2026

Summary

Meta system design round, one question about building a Q&A platform like Quora. Pretty open-ended and I spent way too long on the data model before the interviewer nudged me toward scale.

Questions Asked (1)

Design a question and answer website similar to Quora.

System DesignData ModelingProduct Sense & Ideation

Author's notes

I jumped straight into the feed ranking logic because it felt impressive, but that backfired.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then estimate scale (users, QPS, storage). Propose a high-level architecture covering core services, data models, and APIs, and dive into 1-2 deep areas like feed generation or search. Discuss trade-offs and how you'd evolve the design.

Pro tip: Meta values product sense and scale: tie technical decisions to user impact and growth, and proactively discuss trade-offs (e.g., consistency vs. availability) and how you'd measure success.

1. Clarify Requirements

Ask about core features (Q&A, voting, comments, feed, search, notifications), scale (DAU, QPS, data size), and non-functional needs (latency, availability, consistency).

2. Estimate Scale

Estimate read/write QPS, storage for questions/answers, and bandwidth. Use numbers to justify design choices like sharding or caching.

3. High-Level Design

Sketch architecture: client, API gateway, services (user, question, answer, feed, search, notification), data stores (SQL/NoSQL, cache, search index), and message queue.

4. Data Model & APIs

Define schemas for users, questions, answers, votes, comments, and relationships. Outline key APIs (e.g., POST /question, GET /feed).

5. Deep Dive & Trade-offs

Pick 1-2 areas (e.g., feed generation, search ranking, consistency) and discuss algorithms, scaling, and trade-offs. Mention monitoring and iteration.

Key Points to Mention

Sharding and replication for scalability (e.g., by user ID or question ID)
Caching strategies (Redis/Memcached) for hot data like feeds and popular questions
Feed generation: fan-out on write vs. read, ranking by relevance/recency
Search: inverted index, Elasticsearch, ranking signals (votes, views, freshness)
Data consistency: eventual consistency for feeds, strong consistency for votes
Monitoring, metrics, and A/B testing for product iteration

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