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OpenAI System Design Interview: Complete Preparation Guide

Master OpenAI's system design interview with AI platform questions, evaluation criteria, expert tips, and a 4-week preparation plan.

22 minUpdated Apr 25, 2026
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Interview format

5 rounds total.

System Design60 min

Design an AI platform, model serving, or developer tool system. OpenAI values understanding of ML infrastructure, API design, and building products that put AI in the hands of developers.

Coding 145 min

Algorithmic problem-solving, often involving data processing, distributed systems, or ML pipeline challenges.

Coding 245 min

Second round with potential focus on systems programming, performance optimization, or API implementation.

Technical Deep Dive60 min

In-depth discussion of ML systems, infrastructure architecture, or product engineering challenges.

Mission & Values45 min

Behavioral round assessing alignment with OpenAI's mission of ensuring AGI benefits all of humanity.

Commonly asked systems

Design a model serving API with multi-model routing and rate limitingDesign a ChatGPT-like conversational AI productDesign a plugin and tool-use execution framework for LLMsDesign a fine-tuning platform for custom model trainingDesign a real-time streaming response system for LLM APIsDesign a model safety and content moderation pipelineDesign a developer platform with usage billing and quota managementDesign a retrieval-augmented generation (RAG) system

What they evaluate

ML Infrastructure MasteryHigh

OpenAI operates at the frontier of ML. Demonstrate deep understanding of model serving, training infrastructure, and GPU cluster management.

API & Platform DesignHigh

OpenAI's API serves millions of developers. Show expertise in API design, rate limiting, billing, and developer experience.

Scale & PerformanceHigh

GPT models serve billions of requests. Design for extreme scale with efficient resource utilization and low latency.

Product ThinkingMedium-High

OpenAI ships products (ChatGPT, API). Show you can bridge the gap between ML infrastructure and user-facing products.

Safety AwarenessMedium

Content safety and model alignment matter. Demonstrate how safety considerations influence system architecture.

Tips

  • ▸Understand LLM serving at scale: batching (continuous batching), KV cache optimization, speculative decoding, and model routing
  • ▸Study API platform design: token-based billing, rate limiting (per-user, per-org), API key management, and streaming responses
  • ▸Know the fine-tuning pipeline: dataset preparation, training job management, model validation, and deployment
  • ▸Be familiar with RAG systems: vector databases, embedding models, retrieval strategies, and context window management
  • ▸Study tool use and function calling: how LLMs interact with external tools, sandboxed execution, and output validation
  • ▸Prepare to discuss content moderation: automated classifiers, policy enforcement, and handling adversarial inputs
  • ▸Understand GPU cluster economics: spot vs on-demand, multi-tenant serving, and cost optimization strategies
  • ▸OpenAI moves fast — prepare stories about shipping under ambiguity and iterating rapidly

Preparation roadmap

Week 1-2AI Platform Foundations
  • ·Study LLM serving infrastructure: batching, KV cache, model parallelism, inference optimization
  • ·Learn API platform design: rate limiting, billing, authentication, and streaming
  • ·Review vector databases and RAG architecture patterns
Week 3-4Core AI Platform Systems
  • ·Design a model serving API with multi-model routing
  • ·Design a conversational AI product (ChatGPT-like)
  • ·Design a fine-tuning platform for custom model training
Week 5-6Advanced Features
  • ·Design a plugin and tool-use execution framework
  • ·Design a content safety and moderation pipeline
  • ·Study GPU cluster management: scheduling, fault tolerance, utilization optimization
Week 7-8Mock Interviews & Polish
  • ·Complete 4+ mock system design interviews with AI infrastructure focus
  • ·Practice explaining LLM serving and API platform architectures
  • ·Read OpenAI's system cards, research posts, and API documentation
  • ·Prepare mission-aligned stories about building transformative AI responsibly
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