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

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Apr 2026

Summary

System design round at xAI for a software engineer role. The whole session was basically one big question about building a notification system, and they pushed pretty hard on the fan-out problem and failure handling. Left feeling like I covered the surface but maybe didn't go deep enough on some of the storage tradeoffs.

Questions Asked (1)

Design a notification system for a social platform where posting triggers push notifications to all of a user's followers. Walk through architecture, scale, reliability, and extensibility to other channels.

System DesignTechnical Trade-offsData Modeling

Author's notes

I started with the happy path and that was probably a mistake.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., number of users, followers per user, notification volume), then propose a high-level architecture with decoupled services for posting, fan-out, and delivery. Dive into trade-offs between push vs pull models, reliability mechanisms, and extensibility to other channels like email/SMS.

Pro tip: Emphasize idempotency and deduplication to handle retries and avoid spamming users, and discuss how to prioritize notifications (e.g., real-time vs batched) based on user preferences and system load.

1. Clarify Requirements and Scale

Ask about expected user base, average followers per user, peak posting rate, and latency requirements. Estimate notification volume (e.g., 1M posts/day * 1000 followers = 1B notifications/day).

2. High-Level Architecture

Propose a decoupled system: Post Service publishes events to a message queue (e.g., Kafka). Fan-out Service consumes events, fetches followers, and enqueues notifications. Delivery Service handles push to devices via APNs/FCM.

3. Scale and Performance

Discuss partitioning (e.g., by user ID), caching follower lists, and using a push model for active users vs pull for inactive. Consider batching and rate limiting to handle spikes.

4. Reliability and Fault Tolerance

Ensure at-least-once delivery with idempotent consumers, dead-letter queues for failures, and retries with exponential backoff. Monitor queue depths and delivery success rates.

5. Extensibility to Other Channels

Abstract notification channels (push, email, SMS) with a common interface. Use a channel preference service to route notifications based on user settings and fallback options.

Key Points to Mention

Fan-out on write vs fan-out on read: trade-offs between latency, storage, and complexity.
Message queue (e.g., Kafka) for decoupling and handling backpressure.
Idempotency and deduplication to prevent duplicate notifications.
Rate limiting and batching to avoid overwhelming users and systems.
User preferences and channel prioritization for extensibility.
Monitoring and alerting for delivery failures and queue health.

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