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

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Jun 2026

Summary

System design round at OpenAI for an infra role, focused entirely on building a GitHub Actions-style CI/CD platform from scratch. Pretty grueling scope for a single session and I came out feeling like I'd only scratched the surface on half the topics.

Questions Asked (1)

Design a multi-tenant CI/CD platform similar to GitHub Actions at scale. Walk through the workflow definition model, how jobs form a DAG, runner pool architecture, scheduling fairness, log streaming, artifact caching, secrets management, retries, timeouts, and observability.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was basically the entire interview compressed into one prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., number of tenants, jobs/day, concurrency) and then present a high-level architecture before diving into each component. Structure your answer around the workflow lifecycle: definition, scheduling, execution, and observability, emphasizing trade-offs and multi-tenant isolation at each layer.

Pro tip: Explicitly discuss how you would handle noisy neighbors and resource fairness across tenants, as this is a critical challenge in multi-tenant systems and demonstrates deep operational thinking.

1. Clarify Requirements and Scale

Ask questions to understand expected scale (tenants, jobs/day, concurrency), isolation requirements, and SLAs. This ensures your design targets the right constraints.

2. Define Workflow Model and DAG

Explain how workflows are defined (e.g., YAML) and how jobs form a DAG with dependencies. Discuss validation, versioning, and how the DAG is scheduled.

3. Design Runner Pool and Scheduling

Describe the runner pool architecture (e.g., Kubernetes-based, auto-scaling), job scheduling with fairness (e.g., weighted fair queuing, quotas), and isolation (e.g., per-tenant namespaces).

4. Address Execution Concerns

Cover log streaming (e.g., WebSocket, chunked uploads), artifact caching (e.g., content-addressable storage, cache invalidation), secrets management (e.g., vault integration, short-lived tokens), retries, and timeouts.

5. Ensure Observability and Reliability

Discuss monitoring, tracing, and alerting for the platform. Include how to handle failures, backpressure, and multi-tenant metrics.

Key Points to Mention

Multi-tenant isolation strategies: resource quotas, network policies, and sandboxing to prevent cross-tenant interference.
DAG scheduling: topological sort, parallelism limits, and dependency resolution with failure handling.
Runner pool auto-scaling: dynamic provisioning based on queue depth, with warm pools for latency-sensitive jobs.
Fair scheduling: weighted fair queuing or hierarchical scheduling to prevent noisy neighbors and ensure tenant fairness.
Log streaming and artifact caching: efficient real-time log delivery and cache eviction policies for performance.
Secrets management: secure injection, rotation, and audit logging without exposing secrets to logs or artifacts.

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