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

Senior
Jun 2026

Summary

Got a system design round at OpenAI for a SWE role and the prompt was to design the OpenAI Playground itself. Pretty meta, and I wasn't sure if that was a good or bad sign.

Questions Asked (1)

Q1

Design the OpenAI Playground: walk through the architecture of a web-based interface that lets users interact with language models in real time.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

The meta aspect of this threw me a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then propose a high-level architecture that separates the frontend, backend, and model serving layers. Focus on real-time interaction, scalability, and trade-offs between streaming and batch responses, while emphasizing reliability and cost efficiency.

Pro tip: Highlight the importance of streaming responses and graceful degradation—showing you understand both user experience and backend resilience will set you apart. Also, mention how you'd handle rate limiting and model versioning to demonstrate production maturity.

1. Clarify Requirements

Ask about expected user load, latency requirements, supported models, and whether features like conversation history or file uploads are needed. This ensures your design aligns with actual needs.

2. High-Level Architecture

Outline the main components: a web frontend (React), an API gateway, a backend service (e.g., Python/Node.js) that manages sessions and calls the model API, and a model serving layer. Emphasize separation of concerns.

3. Real-Time Interaction

Explain how to achieve real-time streaming using WebSockets or Server-Sent Events (SSE) from the backend to the frontend, and how the backend streams tokens from the model API. Discuss handling partial responses and errors.

4. Scalability & Reliability

Describe scaling strategies: load balancing, horizontal scaling of backend services, caching frequent requests, and using queues for long-running tasks. Mention rate limiting, retries, and fallback models for reliability.

5. Trade-offs & Optimizations

Discuss trade-offs between streaming and batch, cost vs. latency, and consistency vs. availability. Suggest optimizations like prompt caching, model distillation, and edge caching for static assets.

Key Points to Mention

  • Streaming responses via WebSockets or SSE for real-time UX
  • Backend as a proxy to handle authentication, rate limiting, and model selection
  • Scalability through stateless services, load balancing, and auto-scaling
  • Caching strategies for prompts and responses to reduce cost and latency
  • Error handling and graceful degradation (e.g., fallback to smaller models)
  • Security considerations: API key management, input sanitization, and abuse prevention

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