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

Staff
Apr 2026

Summary

Meta EM interview, one system design question and that was basically it. Pretty standard for the role but still a lot to cover in a single session.

Questions Asked (1)

Q1

Design a video sharing and streaming platform similar to YouTube.

System DesignTechnical Trade-offsData Modeling
Author's notes

There's a lot of surface area here and I think I spread myself too thin early on.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then estimate scale (e.g., daily uploads, views, storage) to drive design decisions. Propose a high-level architecture covering upload, transcoding, storage, CDN, and metadata, then dive deep into 2-3 critical components like video processing pipeline or recommendation system. Discuss trade-offs (e.g., consistency vs. availability, cost vs. latency) and justify choices.

Pro tip: Emphasize how you'd handle the unique challenges of video at scale—like adaptive bitrate streaming, efficient transcoding, and CDN edge caching—and tie them back to user experience metrics (e.g., startup time, rebuffering).

1. Requirements Clarification

Ask clarifying questions to define scope: core features (upload, view, search, recommendations), scale (DAU, uploads/day, storage), and non-functional needs (latency, availability, consistency).

2. Scale Estimation

Estimate key metrics: daily active users, uploads per day, average video size, storage growth, bandwidth, and QPS for reads/writes. Use these to inform component sizing and technology choices.

3. High-Level Design

Sketch the end-to-end architecture: client uploads to object storage, transcoding service processes videos into multiple resolutions, metadata stored in a database, and content served via CDN with adaptive bitrate streaming.

4. Deep Dive into Critical Components

Pick 2-3 areas to detail: e.g., video transcoding pipeline (parallel processing, job queue), storage tiering (hot vs. cold), CDN strategy (edge caching, geo-distribution), or recommendation system (collaborative filtering, real-time updates).

5. Trade-offs and Bottlenecks

Discuss trade-offs (e.g., strong vs. eventual consistency for view counts, cost of transcoding vs. quality), identify bottlenecks (e.g., transcoding queue, database hotspots), and propose mitigations (e.g., sharding, caching, rate limiting).

Key Points to Mention

  • Video transcoding pipeline with parallel processing and job queues (e.g., using Kafka, SQS) to handle uploads asynchronously.
  • Storage strategy: object storage (e.g., S3) for raw and transcoded videos, with tiered storage (hot/warm/cold) for cost efficiency.
  • CDN and adaptive bitrate streaming (e.g., HLS, DASH) to deliver low-latency, high-quality video globally.
  • Metadata and search: use a distributed database (e.g., Cassandra) for video metadata and a search engine (e.g., Elasticsearch) for discovery.
  • Recommendation system: leverage user behavior data, collaborative filtering, and possibly deep learning models for personalized suggestions.
  • Scalability and reliability: sharding, replication, caching (Redis), and monitoring to handle millions of concurrent viewers.

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