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

SeniorPrefer not to say

Jun 2026

Summary

Meta system design round for a software engineer role. One meaty question about building an auction platform from scratch, covering basically every layer of the stack. Felt pretty well-prepared but the bid-spike-at-end-of-auction angle tripped me up more than I expected.

Questions Asked (1)

Design a scalable online auction service where users can create auctions, place bids, view current standings, and receive near-real-time notifications when bids change or auctions close.

System DesignTechnical Trade-offsData Modeling

Author's notes

I started with the API surface and data model which felt solid, but I underestimated how much time they'd want on the correctness angle.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates real-time bidding from auction management and notifications. Focus on scalability, consistency, and low-latency updates, using appropriate data stores and messaging systems. Discuss trade-offs and justify your choices.

Pro tip: Emphasize idempotency and race condition handling in bid processing, as auctions are highly concurrent. Also, consider using a time-series database or in-memory store for real-time bid standings to ensure fast reads.

1. Clarify Requirements

Ask about scale (users, auctions, bids per second), latency requirements, consistency needs, and notification mechanisms (push, pull, email).

2. High-Level Design

Sketch components: API gateway, auction service, bid service, notification service, databases, and message queues. Define data flow for creating auctions, placing bids, and notifying users.

3. Deep Dive into Critical Components

Detail bid processing (atomicity, ordering), real-time updates (WebSockets, pub/sub), and auction closure (scheduled jobs, distributed locks). Discuss data models for auctions, bids, and users.

4. Scalability and Reliability

Explain how to scale each component (sharding, replication, caching), handle failures (retries, idempotency), and ensure consistency (eventual vs strong).

5. Trade-offs and Wrap-up

Summarize key trade-offs (e.g., consistency vs availability, push vs pull notifications) and propose monitoring and metrics.

Key Points to Mention

Use of WebSockets or Server-Sent Events for near-real-time notifications to clients.
Idempotent bid processing and optimistic concurrency control to handle race conditions.
Sharding auctions by auction ID or user ID to distribute load.
Caching current bid standings in Redis or similar for low-latency reads.
Message queue (e.g., Kafka) for decoupling bid processing and notifications.
Scheduled jobs or timers for auction closure, with distributed locking to avoid duplicate closures.

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