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. The whole thing was focused on designing a CI/CD platform from scratch, think GitHub Actions or Jenkins, and they wanted you to go deep on basically every layer of the stack.

Questions Asked (1)

Design a CI/CD platform similar to GitHub Actions or Jenkins. Walk through how you'd handle build triggers, user-defined pipelines, isolated job execution, log streaming, artifact storage, deploy approvals, scheduling across thousands of concurrent builds, secrets management, and sandboxing for long-running jobs.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is a massive question and I think I underestimated the scope for the first few minutes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., thousands of concurrent builds, multi-tenant, security). Then walk through the architecture end-to-end: trigger ingestion, pipeline orchestration, job scheduling and isolation, log/artifact handling, secrets, and deployment approvals. Emphasize trade-offs and how you'd ensure reliability, scalability, and security at each layer.

Pro tip: Anchor your design around a control plane (orchestration, scheduling, API) and a data plane (job execution, logs, artifacts) separation, and explicitly discuss how you'd handle failure domains and multi-tenancy isolation—this shows production maturity.

1. Clarify Requirements and Scale

Ask about expected concurrency, build duration, security/compliance needs, and supported triggers. Define SLAs for scheduling latency, log freshness, and artifact durability.

2. Design Trigger and Pipeline Definition Layer

Explain how events (webhooks, schedules, manual) are ingested and validated, and how user-defined pipelines (YAML/DSL) are parsed, versioned, and stored. Discuss validation and dependency resolution.

3. Architect Job Scheduling and Execution

Describe a distributed scheduler that matches jobs to workers based on resource needs, with queues, priorities, and fair-share across tenants. Cover isolated execution via containers/VMs, sandboxing (seccomp, namespaces), and long-running job handling (checkpointing, timeouts).

4. Handle Logs, Artifacts, and Secrets

Detail log streaming (e.g., WebSocket, chunked HTTP) with backpressure and retention. Explain artifact storage (object store with metadata DB) and secrets management (vault, encryption, short-lived credentials, injection at runtime).

5. Implement Deploy Approvals and Observability

Cover approval workflows (manual gates, policy checks) and how they integrate with the pipeline. Discuss monitoring, tracing, and alerting for build health, plus cost and resource optimization.

Key Points to Mention

Separation of control plane and data plane for scalability and fault isolation
Use of container orchestration (Kubernetes) or custom scheduler for thousands of concurrent jobs
Sandboxing techniques: namespaces, cgroups, seccomp, gVisor, or microVMs for security
Log streaming architecture with backpressure and durable storage (e.g., Kafka, S3)
Secrets management: encryption at rest, short-lived tokens, least privilege, audit logging
Deploy approvals: policy-as-code, manual gates, and integration with CI/CD pipeline state

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