Design a Privacy-Preserving Inference Pipeline

Last updated: May 21, 2025

Quick Overview

Design a system that runs LLM inference on user code without exposing sensitive source code to third parties. Consider on-device vs cloud inference, data anonymization, trust boundaries, and compliance requirements.

Cursor
System Design
Software Engineer
Cursor
May 21, 2025
Software Engineer
Onsite - System Design
System Design
Hard

15

11

1,856 solved


Design a system that runs LLM inference on user code without exposing sensitive source code to third parties. Consider on-device vs cloud inference, data anonymization, trust boundaries, and compliance requirements.

Privacy is a critical concern for Cursor's enterprise customers. Many companies cannot send their source code to third-party APIs. This question tests your ability to design inference infrastructure that respects privacy constraints while maintaining prediction quality. The interviewer expects discussion of on-device inference, proxy architectures, and data handling policies.

What the Interviewer Expects
  • Define clear trust boundaries between the editor, Cursor's servers, and model providers
  • Design an architecture that supports both cloud and on-device inference
  • Propose data anonymization techniques for code sent to cloud models
  • Discuss compliance requirements like SOC 2, GDPR, and enterprise data residency
  • Address the quality vs privacy trade-off with concrete solutions
Key Topics to Cover
Privacy-preserving machine learning
On-device vs cloud inference trade-offs
Data anonymization and redaction
Enterprise compliance and SOC 2
Trust boundary architecture
How to Approach This
  1. Start by clarifying functional and non-functional requirements with the interviewer.
  2. Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
  3. Draw a high-level architecture first, then deep dive into 1-2 critical components.
  4. Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
  5. Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
  • How would you handle a customer that requires all data to stay within their AWS VPC?
  • How would you measure whether anonymization degrades prediction quality?
  • What is your approach to key management and encryption at rest?
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