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Meta·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

ML engineer system design round at Meta, one question about building a recommendation system. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

Design a product recommendation system.

System DesignTechnical Trade-offsProduct Sense & Ideation
Author's notes

I jumped straight into collaborative filtering and the interviewer just kind of waited.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and requirements, then outline a high-level system architecture covering data, model, and serving layers. Dive into key components like candidate generation and ranking, discussing trade-offs and evaluation metrics. Conclude by addressing scalability, latency, and potential improvements.

Pro tip: Emphasize the importance of defining clear success metrics (e.g., CTR, engagement) and how they tie to business goals. Show awareness of real-world constraints like cold start and scalability from the beginning.

1. Clarify Requirements

Ask questions to understand the product context, scale, and objectives. Define what a 'recommendation' means and the key metrics for success.

2. High-Level Architecture

Sketch the end-to-end system: data ingestion, feature engineering, model training, and serving. Mention offline vs. online components.

3. Model Design

Describe the recommendation approach, e.g., two-stage (candidate generation + ranking). Discuss model choices (collaborative filtering, deep learning) and features.

4. Evaluation & Metrics

Explain offline metrics (precision, recall) and online metrics (CTR, engagement). Discuss A/B testing and how to handle feedback loops.

5. Scalability & Trade-offs

Address latency, throughput, and cost. Discuss trade-offs like model complexity vs. inference speed, and how to handle cold start and scalability.

Key Points to Mention

  • Two-stage architecture: candidate generation (e.g., matrix factorization, ANN) and ranking (e.g., deep neural networks).
  • Feature engineering: user, item, and context features; embeddings for high-cardinality IDs.
  • Evaluation: offline metrics (precision@k, recall@k) and online metrics (CTR, conversion rate, watch time).
  • Cold start problem: strategies like content-based filtering, using side information, and exploration.
  • Scalability: distributed training, model serving with low latency (e.g., using GPUs, caching), and real-time updates.
  • Trade-offs: model complexity vs. latency, batch vs. real-time inference, and personalization vs. privacy.

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