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

Staff
Jun 2026

Summary

Interviewed for an EM role at OpenAI and got a system design question about building a notification system. Pretty classic for this type of role but the scope they expected was broader than I anticipated.

Questions Asked (1)

Q1

Design a notification system.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

Went in thinking I'd cover the basics and move on but they kept pushing on scale, delivery guarantees, and how you handle things like rate limiting per user or per channel.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture covering ingestion, processing, and delivery. Dive into key components like message queues, fan-out, and delivery guarantees, and discuss trade-offs such as consistency vs. availability and push vs. pull.

Pro tip: Emphasize idempotency and deduplication to handle retries and ensure exactly-once delivery, and discuss how to prioritize notifications (e.g., critical vs. promotional) to avoid overwhelming users.

1. Clarify Requirements

Ask about scale (users, notifications per second), types (email, push, SMS), latency, delivery guarantees, and priorities. Define functional and non-functional requirements.

2. High-Level Design

Sketch components: API gateway, notification service, message queue, workers, and external providers. Explain data flow from ingestion to delivery.

3. Deep Dive into Components

Detail how to handle fan-out (e.g., for a user with many followers), use queues for decoupling, and manage retries with exponential backoff. Discuss storage for user preferences and notification history.

4. Address Trade-offs and Scalability

Discuss trade-offs: push vs. pull, synchronous vs. asynchronous, at-least-once vs. exactly-once. Explain scaling strategies like sharding, partitioning, and rate limiting.

5. Wrap Up with Monitoring and Extensions

Mention monitoring, alerting, and metrics. Suggest extensions like A/B testing, personalization, or analytics.

Key Points to Mention

  • Message queues (e.g., Kafka, RabbitMQ) for decoupling and buffering
  • Fan-out strategies (e.g., write fan-out vs. read fan-out) and handling hot users
  • Delivery guarantees (at-least-once, exactly-once) and idempotency
  • Rate limiting and prioritization to prevent spam and ensure critical notifications
  • Storage for user preferences, templates, and notification logs
  • Monitoring, retries, and dead-letter queues for reliability

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