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

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May 2026

Summary

ML engineer system design round at Meta, one question about building a next word prediction system. Pretty open-ended and I wasn't fully sure how deep to go on the ML side versus the infrastructure side.

Questions Asked (1)

Design a next word prediction system.

System DesignTechnical Trade-offs

Author's notes

I started with the obvious stuff, n-gram models, then moved to neural approaches, but I kept second-guessing how much time to spend on model architecture versus serving latency.

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Suggested Approach

Start by clarifying the scope and requirements (e.g., mobile keyboard vs. search bar, latency, personalization). Then outline a high-level architecture covering data, model, training, and serving, and dive into trade-offs for key components like model choice and serving infrastructure.

Pro tip: Emphasize the latency-accuracy trade-off and propose a two-stage approach: a fast candidate generator (e.g., n-gram or small neural model) followed by a more accurate ranker (e.g., large transformer) only when latency permits.

1. Clarify Requirements

Ask about the use case (e.g., mobile keyboard, search bar), latency constraints, scale (QPS), personalization needs, and evaluation metrics (e.g., top-1 accuracy, perplexity).

2. High-Level Architecture

Outline the end-to-end system: data collection and preprocessing, model training, model serving, and client integration. Mention offline vs. online components.

3. Model Selection & Training

Discuss model choices (n-gram, RNN, Transformer) and trade-offs. Cover training data (user history, global corpus), techniques (e.g., distillation, quantization), and personalization.

4. Serving & Latency Optimization

Explain how to serve predictions with low latency: caching, batching, model compression, and fallback strategies. Consider edge vs. cloud deployment.

5. Evaluation & Iteration

Define offline metrics (perplexity, accuracy@k) and online metrics (CTR, user engagement). Discuss A/B testing and continuous improvement.

Key Points to Mention

Latency-accuracy trade-off and two-stage prediction (candidate generation + ranking)
Model choices: n-gram, RNN/LSTM, Transformer, and their pros/cons for next word prediction
Personalization: incorporating user history and context while handling privacy
Serving infrastructure: caching, batching, model quantization, and edge deployment
Evaluation metrics: offline (perplexity, top-k accuracy) and online (CTR, latency)
Handling scale: distributed training, large-scale data processing, and real-time inference

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