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

SeniorPrefer not to say
May 2026

Summary

Did a system design round at OpenAI for a software engineer role. First round felt rough, got drilled with follow-up questions I wasn't ready for, and pacing turned out to be a bigger issue than I expected.

Questions Asked (1)

Q1

Design a social network system.

System DesignTechnical Trade-offsData Modeling
Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a high-level architecture covering core features like user profiles, posts, and feeds. Dive into data modeling and trade-offs for scalability, consistency, and availability, focusing on feed generation and storage choices.

Pro tip: Emphasize the trade-offs between fan-out on write vs. read for feed generation, and discuss how to handle hot users and celebrity problem. Show awareness of cost and latency implications.

1. Clarify Requirements

Ask questions to understand functional and non-functional requirements: scale (DAU, QPS), features (friends, posts, feed, messaging), consistency vs. availability, latency targets.

2. High-Level Design

Sketch the main components: clients, API gateway, services (user, post, feed, graph), databases, caches, and message queues. Explain data flow for key operations like posting and viewing feed.

3. Data Modeling

Design schemas for users, friendships (graph), posts, and feeds. Discuss SQL vs. NoSQL choices, sharding strategies, and indexing for efficient queries.

4. Deep Dive into Feed Generation

Compare fan-out on write vs. read, hybrid approach, and handling of celebrities. Discuss caching, ranking, and pagination.

5. Scalability and Trade-offs

Address scaling bottlenecks: database sharding, caching layers, CDN for media, and consistency models. Discuss trade-offs like latency vs. consistency, and cost.

Key Points to Mention

  • Fan-out on write vs. read for feed generation and hybrid approach
  • Sharding and replication strategies for databases (e.g., user data, posts)
  • Caching strategies (Redis, Memcached) for hot data and feeds
  • Handling the celebrity/hot user problem (e.g., pull-based for celebrities)
  • Consistency models: eventual consistency for feeds, strong consistency for user data
  • Use of message queues (Kafka) for asynchronous processing and decoupling

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