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

SeniorPrefer not to say
Apr 2026

Summary

System design round at OpenAI for a backend engineer role, focused entirely on building a cloud-based remote dev environment service from scratch. Pretty broad scope for a single session and I felt like I was juggling too many subsystems at once.

Questions Asked (1)

Q1

Design a system that provisions on-demand remote development environments in the cloud. Walk through the full lifecycle: creating, starting, stopping, and destroying a devbox, and cover compute, storage, networking, IDE access, multi-tenant isolation, autoscaling, and cost controls.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then walk through the devbox lifecycle from creation to destruction, covering compute, storage, networking, IDE access, isolation, autoscaling, and cost controls. Emphasize trade-offs and how you would handle multi-tenancy and cost efficiency at scale.

Pro tip: Highlight the importance of fast provisioning and secure isolation, and propose using Kubernetes with namespaces and resource quotas for multi-tenancy, as it balances isolation and efficiency. Also, mention how you would implement idle detection to automatically stop devboxes and save costs.

1. Clarify Requirements and Constraints

Ask about expected scale, latency requirements, security/compliance needs, and budget constraints. This shapes decisions on compute, storage, and isolation.

2. Design the Lifecycle API and Orchestration

Define REST/gRPC APIs for create, start, stop, destroy. Use an orchestrator (e.g., Kubernetes) to manage devbox pods, persistent volumes, and networking.

3. Detail Compute, Storage, and Networking

Choose instance types (spot vs on-demand), persistent storage (EBS/EFS), and networking (VPC, security groups). Ensure IDE access via web-based IDE (e.g., code-server) or SSH with port forwarding.

4. Address Multi-Tenancy and Isolation

Use namespaces, network policies, and resource quotas for isolation. Consider VM-level isolation for stronger security. Implement authentication and authorization.

5. Implement Autoscaling and Cost Controls

Autoscale based on CPU/memory or queue length. Implement idle detection to stop devboxes, use spot instances, and set budgets/alerts. Provide cost visibility per user/team.

Key Points to Mention

  • Use Kubernetes with namespaces and resource quotas for multi-tenant isolation and efficient resource sharing.
  • Persistent storage (e.g., EBS volumes) that survives stop/start, and snapshotting for backup.
  • Networking: VPC, security groups, and ingress for IDE access (e.g., via HTTPS with authentication).
  • Autoscaling: cluster autoscaler and horizontal pod autoscaler based on metrics; consider scale-to-zero for idle devboxes.
  • Cost controls: spot instances, idle detection, budget alerts, and showback/chargeback per team.
  • Security: IAM roles, network policies, and secrets management for IDE access and data protection.

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