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

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Apr 2026

Summary

System design round at OpenAI for a software engineer role, focused entirely on designing a cloud-based IDE from scratch. Dense question with a lot of surface area, felt like I was triaging the whole time rather than going deep on anything.

Questions Asked (1)

Design a cloud-based IDE similar to Codespaces or Replit. Walk through containerized workspace provisioning per user, file system persistence, real-time editor sync, terminal and language server multiplexing, sandboxed build and run environments, collaborative editing, and how you'd handle cost and idle workspaces at scale.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This question is basically five system design questions stapled together.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then present a high-level architecture with core components like container orchestration, persistent storage, and real-time sync. Dive into each area (provisioning, persistence, sync, multiplexing, sandboxing, collaboration, cost) with trade-offs and justify your choices. Conclude with how you'd handle scale and cost, emphasizing idle detection and resource optimization.

Pro tip: Emphasize the importance of separating the control plane (orchestration, API) from the data plane (user containers) to achieve scalability and fault isolation. Also, mention using Kubernetes with custom operators for provisioning and a distributed file system like Ceph or cloud-native options (e.g., EFS) for persistence.

1. Clarify Requirements and Scale

Ask about expected number of concurrent users, workspace size, latency requirements, and budget constraints. This informs technology choices and trade-offs.

2. High-Level Architecture

Outline the main components: API gateway, orchestration layer (Kubernetes), container registry, persistent storage, real-time sync service, terminal/LSP multiplexer, and collaboration service. Explain how they interact.

3. Deep Dive into Key Areas

For each area (provisioning, persistence, sync, multiplexing, sandboxing, collaboration), describe the design, technologies (e.g., Docker, Kubernetes, CRDTs, WebSockets), and trade-offs (e.g., latency vs. consistency, cost vs. performance).

4. Scalability and Cost Optimization

Discuss how to scale horizontally, handle idle workspaces (e.g., auto-stop after inactivity, snapshot to cold storage), and optimize resource usage (e.g., bin packing, spot instances).

5. Wrap Up with Trade-offs and Future Work

Summarize key decisions, acknowledge limitations, and suggest potential improvements or monitoring strategies.

Key Points to Mention

Container orchestration with Kubernetes and custom operators for per-user workspace provisioning, including resource quotas and isolation.
Persistent storage solutions: network file systems (e.g., EFS, Ceph) or block storage with snapshots for durability and fast restores.
Real-time editor sync using operational transformation (OT) or conflict-free replicated data types (CRDTs), with WebSocket for low-latency communication.
Terminal and language server multiplexing: using a single container with multiple processes, or sidecars, and routing via a reverse proxy (e.g., Envoy) with session management.
Sandboxed build/run environments: using gVisor, Kata Containers, or seccomp for security, and ephemeral containers for builds.
Cost and idle handling: auto-stop idle workspaces, use spot instances, and implement tiered storage (hot vs. cold) with snapshots to reduce costs.

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