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

SeniorPrefer not to say

Jun 2026

Summary

Google system design round, one question about building autocomplete for iMessage. Pretty open-ended, not much else to go on.

Questions Asked (1)

Design an autocomplete system for iMessage.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

Spent the first few minutes just trying to scope it down because autocomplete can mean a lot of things.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level architecture that separates client-side prediction from server-side aggregation. Focus on the unique aspects of iMessage: on-device processing for privacy, low-latency suggestions, and personalization based on user behavior. Discuss trade-offs between local and remote models, and how to handle cold start and scalability.

Pro tip: Emphasize privacy-preserving techniques like federated learning and on-device inference, as iMessage is end-to-end encrypted and users expect their data to stay private. Also, consider the product context: autocomplete should feel seamless and non-intrusive, so latency and relevance are critical.

1. Clarify Requirements and Scope

Ask questions to understand the expected scale (e.g., number of users, messages per day), latency requirements (e.g., <100ms), and privacy constraints (e.g., on-device processing). Define what 'autocomplete' means: predicting the next word, phrase, or full message?

2. High-Level Architecture

Propose a client-server split: on-device model for immediate predictions and a server-side system for aggregating anonymized data to improve global models. Include components like a personalization layer, a language model, and a ranking system.

3. Data and Model Design

Discuss data sources: user's past messages (on-device), contact names, and common phrases. Choose models: n-gram or neural (e.g., LSTM, Transformer) for on-device, and larger models server-side. Address training: federated learning to update global models without compromising privacy.

4. Scalability and Latency

Explain how to handle millions of users: sharding, caching frequent predictions, and using lightweight models on-device. Ensure low latency by precomputing suggestions and using efficient data structures (e.g., trie).

5. Evaluation and Iteration

Define metrics: accuracy, latency, user engagement (e.g., acceptance rate). Discuss A/B testing and how to handle feedback loops. Mention fallback mechanisms if predictions are poor.

Key Points to Mention

Privacy-preserving techniques: on-device inference, federated learning, differential privacy
Latency requirements: sub-100ms for real-time suggestions
Personalization: using user's writing style, frequent contacts, and context (time, location)
Model choices: trade-offs between n-gram (fast, low memory) and neural networks (accurate, resource-intensive)
Scalability: handling billions of messages with distributed systems and caching
Product sense: non-intrusive UI, ranking suggestions by relevance, and handling multi-language support

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