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

SeniorPrefer not to say
Jun 2026

Summary

System design round at Microsoft for a software engineer role, single question but it sprawled into basically every corner of backend design you can think of. Dense session, left feeling like I covered maybe 60% of what they were looking for.

Questions Asked (1)

Q1

Design a re-engagement system for an AI assistant product that handles both proactive notifications to inactive users and in-chat reminder extraction and delivery.

System DesignData ModelingTechnical Trade-offs
Author's notes

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Suggested Approach

Start by clarifying requirements and scope, then design a high-level architecture that separates proactive notifications and in-chat reminder extraction, detailing data models, delivery mechanisms, and trade-offs. Emphasize scalability, reliability, and user experience while discussing how to measure success and iterate.

Pro tip: Demonstrate awareness of privacy and user consent, especially for proactive notifications, and propose a feedback loop to continuously improve reminder extraction accuracy based on user interactions.

1. Clarify Requirements and Scope

Ask questions to understand the product, user base, and goals: What defines an inactive user? What types of reminders? What channels? What are latency and accuracy requirements?

2. High-Level Architecture

Outline two main components: a proactive notification system (triggered by inactivity) and an in-chat reminder extraction/delivery system (triggered by user messages). Describe how they interact and share data.

3. Data Modeling and Storage

Define schemas for user activity, reminders, and notifications. Consider using a time-series database for activity, a relational or NoSQL store for reminders, and a queue for delivery.

4. Delivery and Scalability

Design delivery pipelines: for proactive notifications, use a scheduler and push service; for in-chat reminders, use real-time processing (e.g., stream processing) and integrate with chat backend. Discuss partitioning, rate limiting, and retries.

5. Trade-offs and Metrics

Discuss trade-offs: accuracy vs. latency in reminder extraction, frequency vs. user annoyance in notifications. Define metrics: re-engagement rate, reminder accuracy, user satisfaction, and system cost.

Key Points to Mention

  • User activity tracking and inactivity detection (e.g., last active timestamp, session frequency)
  • Reminder extraction techniques: NLP models (e.g., NER, intent detection) and rule-based approaches
  • Notification delivery channels: email, push, SMS, and in-app, with user preferences and throttling
  • Scalability considerations: handling millions of users, real-time processing, and distributed scheduling
  • Privacy and consent: opt-in mechanisms, data anonymization, and compliance (GDPR, CCPA)
  • Feedback loops: using user interactions to improve reminder extraction and notification timing

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