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

Senior
Jun 2026

Summary

System design round at OpenAI for a software engineer role. The problem was open-ended enough that I spent half the time figuring out what to even focus on, and never really got into a groove.

Questions Asked (1)

Q1

Design a cloud-based IDE system.

System DesignTechnical Trade-offsAdaptability & Ambiguity
Author's notes

Drew a rough high-level diagram first, then tried to go deep on worker node lifecycle management.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that separates the IDE frontend, backend services, and infrastructure. Focus on key components like workspace management, code execution, and real-time collaboration, and discuss trade-offs between latency, cost, and scalability.

Pro tip: Emphasize the importance of isolating user code execution securely and efficiently, and discuss how to handle state synchronization for real-time collaboration without compromising performance.

1. Clarify Requirements

Ask questions to understand the scope: target users, supported languages, expected scale, latency requirements, and key features like collaboration or debugging.

2. High-Level Architecture

Outline the main components: web-based frontend, backend services (workspace management, file storage, execution engine), and infrastructure (compute, storage, networking).

3. Deep Dive into Key Components

Detail critical parts: how to manage user workspaces (containers/VMs), execute code securely (sandboxing), and enable real-time collaboration (CRDTs/OT).

4. Address Trade-offs and Scalability

Discuss trade-offs: latency vs. cost for code execution, consistency vs. availability for collaboration, and strategies for scaling (e.g., regional deployments, auto-scaling).

5. Summarize and Iterate

Recap the design, highlight how it meets requirements, and suggest potential improvements or areas for further discussion based on feedback.

Key Points to Mention

  • Use of containers or lightweight VMs for isolated, reproducible workspaces.
  • Secure code execution via sandboxing (e.g., gVisor, Firecracker) and resource limits.
  • Real-time collaboration using CRDTs or operational transformation for conflict-free editing.
  • Efficient file storage and synchronization (e.g., using object storage with caching).
  • Scalability considerations: auto-scaling, load balancing, and multi-region deployment.
  • Latency optimization: edge computing, pre-warming containers, and WebSocket connections.

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