Design a large-scale Image Processing Platform
Last updated: May 2, 2026
Quick Overview
Design a scalable image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
OpenAI
May 2, 202614
5
4,410 solved
Design a scalable image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
OpenAI asks this during the Onsite to assess your depth in software engineering. They want to see understanding of design patterns, system architecture, and the trade-offs involved in different technical approaches.
What the Interviewer Expects
- Apply engineering principles to a realistic design scenario
- Discuss trade-offs between different approaches with concrete examples
- Demonstrate understanding of testability, maintainability, and extensibility
- Connect theoretical concepts to production engineering practices
- Discuss how the approach scales with team and codebase size
Key Topics to Cover
How to Approach This
- Apply SOLID principles. Single Responsibility makes code testable, Open/Closed makes it extensible.
- Choose data structures based on access patterns, not familiarity.
- Prefer immutable data and message passing over shared mutable state for concurrency.
- Design APIs with RESTful conventions, versioning, meaningful errors, and pagination from day one.
Possible Follow-up Questions
- How would you document this for other engineers?
- What testing strategy would you use for this component?
- How would you measure the performance of this component in production?
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Core Design Principles
For the large-scale Image Processing Platform at OpenAI, we will apply the Single Responsibility Principle (SRP) to ensure that each component of the system has one reason to change, making it eas...
Architecture
The architecture for this image processing platform will follow a microservices approach. Each service will handle specific functionality (e.g., upload, processing, storage, and retrieval). We wil...
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