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

Staff

May 2026

Summary

Interviewed for an EM role at OpenAI and got hit with a classic system design question. Not much to report beyond that, but it was a solid exercise in thinking through scale and product constraints at the same time.

Questions Asked (1)

Design a messaging and collaboration platform like Slack.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

I jumped straight into the data model and real-time messaging layer, which felt right, but I spent too long on WebSocket connection handling and barely touched the workspace/channel permission model.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., number of users, messages per day, latency, consistency). Then outline a high-level architecture covering real-time messaging, storage, and search, and dive into key components like WebSocket gateways, message queues, and distributed databases. Finally, discuss trade-offs and potential bottlenecks.

Pro tip: Emphasize the importance of eventual consistency for message ordering and delivery, and propose a hybrid approach using both WebSockets for real-time and HTTP long-polling for fallback. Also, mention how you would handle message search at scale with an inverted index.

1. Clarify Requirements

Ask about scale (DAU, messages per day), latency requirements, consistency needs, and key features (channels, DMs, threads, search, notifications).

2. High-Level Design

Sketch the main components: clients, WebSocket gateways, message service, presence service, channel service, storage (messages, metadata), and search.

3. Deep Dive into Critical Components

Detail how messages are sent, stored, and delivered: use of message queues (e.g., Kafka) for fan-out, distributed databases (e.g., Cassandra) for message storage, and WebSockets for real-time delivery.

4. Address Scalability and Reliability

Discuss partitioning (by channel or user), replication, fault tolerance, and how to handle spikes (e.g., auto-scaling WebSocket servers).

5. Trade-offs and Optimizations

Talk about trade-offs: consistency vs. availability, push vs. pull for notifications, and optimizations like caching, CDN for static assets, and search indexing.

Key Points to Mention

WebSocket for real-time bidirectional communication, with fallback to long-polling
Message queue (e.g., Kafka) for decoupling and handling fan-out to multiple recipients
Distributed storage for messages (e.g., Cassandra) with appropriate partitioning and replication
Presence and typing indicators using a separate service with pub/sub
Search functionality using an inverted index (e.g., Elasticsearch) and indexing pipeline
End-to-end encryption and security considerations for private messages

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