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

Senior

Jun 2026

Summary

System design round at OpenAI for a backend engineer role, centered entirely on building a Slack-style messaging platform. It went deep fast and covered a lot of ground in one question.

Questions Asked (1)

Design a team messaging platform similar to Slack, covering workspaces, channels, direct messages, real-time delivery, message history, search, threads, file uploads, and presence indicators. How do you handle millions of concurrent users with sub-200ms latency and high availability?

System DesignData ModelingTechnical Trade-offs

Author's notes

This is essentially four or five hard problems stapled together and they want you to move through all of them.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates real-time messaging from persistent storage and search. Focus on partitioning strategies, caching, and asynchronous processing to meet latency and availability goals, and discuss trade-offs explicitly.

Pro tip: Anchor your design around a message pipeline: ingest, persist, fan-out, and index. This shows you understand the critical path and can reason about bottlenecks under load.

1. Clarify Requirements and Scale

Ask about user count, message volume, latency targets, consistency needs, and feature priorities. Establish assumptions for millions of concurrent users and sub-200ms delivery.

2. High-Level Architecture

Outline core components: API gateways, WebSocket servers for real-time, message queues, distributed databases, cache layers, search clusters, and file storage. Explain how they interact.

3. Data Modeling and Storage

Design schemas for workspaces, channels, messages, threads, and presence. Choose appropriate databases (e.g., Cassandra for messages, Redis for presence, Elasticsearch for search) and discuss partitioning and replication.

4. Real-Time Delivery and Presence

Detail the pub/sub system for message fan-out, WebSocket connection management, and presence tracking. Address how to handle millions of connections with low latency and high availability.

5. Scalability, Availability, and Trade-offs

Discuss horizontal scaling, sharding, caching strategies, CDN for files, and disaster recovery. Highlight trade-offs between consistency, latency, and cost.

Key Points to Mention

Partitioning messages by channel or workspace to enable horizontal scaling and efficient retrieval.
Using a publish-subscribe system (e.g., Kafka, Redis Pub/Sub) for real-time message fan-out to online users.
Caching frequently accessed data (e.g., recent messages, user profiles) with Redis or Memcached to reduce database load.
Asynchronous indexing of messages into a search engine (e.g., Elasticsearch) to keep the write path fast.
Presence indicators implemented via heartbeats and a fast in-memory store like Redis, with eventual consistency.
File uploads handled via object storage (e.g., S3) with CDN for low-latency downloads and pre-signed URLs for security.

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