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

SeniorPrefer not to say
May 2026

Summary

System design round at OpenAI for a software engineer role. The whole session was basically one massive question about building a cloud IDE from scratch, and they kept pushing deeper on every subsystem I touched.

Questions Asked (1)

Q1

Design a cloud-based IDE (similar to Codespaces or Replit), covering containerized workspace provisioning per user, file system persistence, low-latency collaborative editing, terminal and language server multiplexing, build/run sandboxing, and cost management for idle workspaces.

System DesignTechnical Trade-offs
Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a high-level architecture that separates control plane (orchestration, persistence, collaboration) from data plane (workspaces, terminals, language servers). Dive into each component, emphasizing trade-offs around latency, cost, and isolation, and conclude with a discussion of failure modes and monitoring.

Pro tip: Focus on the cost and latency implications of idle workspaces: propose a tiered hibernation strategy (e.g., pause containers, snapshot to object storage) and explain how you'd measure and optimize cold-start times. This shows you understand real-world constraints beyond just functional design.

1. Clarify Requirements and Scale

Ask about expected number of concurrent users, workspace size, latency targets, budget constraints, and security/compliance needs. This scopes the design and highlights key trade-offs.

2. High-Level Architecture

Sketch a control plane (API gateway, orchestrator, metadata DB, collaboration service) and data plane (workspace containers, persistent volumes, terminal/LSP proxies). Explain how they interact.

3. Deep Dive into Components

Detail provisioning (container orchestration, image caching), persistence (network volumes vs. object storage sync), collaboration (CRDT/OT, WebSocket), terminal/LSP multiplexing (session management, protocol translation), and sandboxing (gVisor, seccomp).

4. Cost Management and Idle Handling

Describe strategies for detecting idle workspaces, hibernating (pause containers, snapshot to S3), and resuming quickly. Discuss trade-offs between cost and cold-start latency.

5. Trade-offs, Failure Modes, and Monitoring

Summarize key trade-offs (e.g., consistency vs. latency, isolation vs. performance), discuss failure scenarios (node failure, network partition), and outline monitoring/alerting for SLOs.

Key Points to Mention

  • Container orchestration (Kubernetes) with per-user pods and persistent volume claims for file system persistence.
  • Collaborative editing using CRDTs or OT with WebSocket for low-latency sync, and conflict resolution strategies.
  • Terminal and language server multiplexing via a proxy that manages multiple sessions and translates protocols (e.g., LSP over WebSocket).
  • Sandboxing techniques (gVisor, seccomp, AppArmor) to isolate build/run processes and prevent escapes.
  • Cost optimization: idle detection, hibernation (pause containers, snapshot to object storage), and tiered storage for cold workspaces.
  • Scalability and fault tolerance: horizontal scaling of control plane, sharding, and handling node failures with rescheduling.

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