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
May 21, 202515
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
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- 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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Practice System Design ProblemsSample Answer
Requirements
Functional Requirements:
- Privacy-Preserving Inference: Users must be able to run LLM inference on their code without exposing any sensitive information to third-party services.
- **On-Devi...
Capacity Estimation
Assuming Cursor has approximately 10,000 active enterprise users, with an average of 50 inference requests per user per day:
- Daily Requests: 10,000 users * 50 requests = 500,000 requests per d...
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