Prep Plan

OpenAI Interview Prep

Complete study plan for OpenAI software engineering interviews. Covers coding problems, AI-scale system design, and OpenAI's unique culture of building AGI safely. Prepare for interviews at the creators of GPT and ChatGPT.

What Is the OpenAI Interview Process?

OpenAI builds some of the most widely-used AI systems in the world, including GPT, ChatGPT, DALL-E, and the OpenAI API platform. The interview runs roughly six rounds across five weeks: recruiter screen, 60-minute live coding, a 48-hour paid take-home work trial, and a final onsite loop with coding, system design, technical project presentation, and behavioral sessions.

What Makes OpenAI Interviews Different?

  • Paid work trial: OpenAI includes a 48-hour paid take-home project, which is unusual among big tech companies. This tests real-world engineering ability, not just algorithm recall.
  • Practical engineering problems: Questions focus on practical engineering primitives — LRU cache, rate limiter, iterator, KV store — rather than algorithmic puzzles. If you’ve prepped by grinding graph DP and combinatorics, you’ve prepared for the wrong interview.
  • Pair-programming format: The coding format is closer to a real pair-programming session than a LeetCode quiz. The interviewer will talk through your approach, push back on assumptions, and add requirements partway through.
  • Higher coding bar: The coding bar runs higher than at most big tech companies and sits closer to what you’d find at other frontier AI labs.
  • Process varies by team: Rounds change between teams and even between candidates for the same team, so expect some variation.

Part 1: Coding Problems

OpenAI coding interviews emphasize practical engineering and clean code over algorithm puzzles. Focus on state management, real-world data handling, and building extensible implementations. Expect the interviewer to add requirements mid-problem.

Arrays & Strings

  1. Two Sum - Easy
  2. Best Time to Buy and Sell Stock - Easy
  3. Product of Array Except Self - Medium
  4. Merge Intervals - Medium
  5. 3Sum - Medium
  6. Spiral Matrix - Medium
  7. Trapping Rain Water - Hard
  8. Longest Substring Without Repeating Characters - Medium
  9. Group Anagrams - Medium
  10. Minimum Window Substring - Hard

Trees & Graphs

  1. Maximum Depth of Binary Tree - Easy
  2. Invert Binary Tree - Easy
  3. Validate Binary Search Tree - Medium
  4. Binary Tree Level Order Traversal - Medium
  5. Kth Smallest Element in a BST - Medium
  6. Serialize and Deserialize Binary Tree - Hard
  7. Number of Islands - Medium
  8. Course Schedule - Medium
  9. Clone Graph - Medium
  10. Word Ladder - Hard

Dynamic Programming

  1. Climbing Stairs - Easy
  2. House Robber - Medium
  3. Coin Change - Medium
  4. Longest Increasing Subsequence - Medium
  5. Word Break - Medium
  6. Decode Ways - Medium

Data Structures

  1. Valid Parentheses - Easy
  2. Min Stack - Medium
  3. Daily Temperatures - Medium
  4. Largest Rectangle in Histogram - Hard
  5. Top K Frequent Elements - Medium
  6. Kth Largest Element in an Array - Medium
  7. Find Median from Data Stream - Hard
  8. Reverse Linked List - Easy
  9. Merge K Sorted Lists - Hard

Sorting, Searching & Backtracking

  1. Search in Rotated Sorted Array - Medium
  2. Median of Two Sorted Arrays - Hard
  3. Combination Sum - Medium
  4. Word Search - Medium

Part 2: System Design — AI Platform Scale

OpenAI system design interviews focus on building infrastructure that serves AI to hundreds of millions of users. The ChatGPT API handles billions of tokens daily, requiring expertise in high-throughput serving, distributed systems, and reliability engineering.

What Does OpenAI Look for in System Design?

  • Clear capacity estimation for AI workloads (tokens/sec, GPU utilization, latency SLAs)
  • Understanding of model serving architecture (batching, caching, routing)
  • Data pipeline design for training and fine-tuning
  • API design for developer-facing platforms
  • Reliability and graceful degradation at scale

Core Fundamentals

  1. URL Shortener — Hashing, database design, read-heavy caching
  2. Rate Limiter — Token-based rate limiting (essential for API platforms)
  3. Distributed Cache — Prompt caching, KV store patterns

OpenAI-Relevant Designs

  1. Slack — Real-time messaging (ChatGPT conversation interface)
  2. Google Docs — Streaming responses, real-time collaboration
  3. Job Scheduler — Training job orchestration, batch processing
  4. Web Crawler — Data collection for training at scale
  5. YouTube — Content processing pipelines, async transcoding (analogous to model inference queues)
  6. Top-K — Token sampling, ranking algorithms
  7. Netflix — Personalization, recommendation systems (GPT Store ranking)

Topics to Study for AI Platform Systems

LLM API Design — Building developer-facing AI platforms:

  • RESTful API design for chat completions, embeddings, fine-tuning
  • Streaming responses (Server-Sent Events for token-by-token delivery)
  • SDK design patterns, retry logic, error handling
  • Usage metering and billing at token granularity

Model Serving at Scale — Inference infrastructure:

  • GPU cluster management and multi-tenant serving
  • Continuous batching for throughput optimization
  • Model routing (selecting model version, capacity-aware load balancing)
  • KV cache management for long-context conversations
  • Speculative decoding and other latency optimizations

Data Infrastructure — Training and evaluation:

  • Petabyte-scale data ingestion, cleaning, deduplication
  • Data quality scoring and filtering pipelines
  • RLHF data collection and human feedback loops
  • Evaluation frameworks for model quality

Reliability Engineering — Keeping AI services available:

  • Graceful degradation when GPU capacity is constrained
  • Circuit breakers for downstream service failures
  • Monitoring: latency percentiles, token throughput, error rates, safety metrics
  • Multi-region deployment for global availability

Supporting Concepts

Supporting Systems

  • Apache Kafka — Event streaming for real-time data pipelines
  • Redis — Prompt caching, session state, rate limiting
  • PostgreSQL — User data, API keys, billing records
  • Elasticsearch — Log aggregation, usage analytics
  • Apache Spark — Data processing for training pipelines

Part 3: Values & Culture — Building AGI Responsibly

OpenAI’s culture rounds assess alignment with their mission and how you think about building powerful AI systems responsibly. These conversations are substantive — interviewers want to understand your genuine perspective, not rehearsed answers.

What Does OpenAI Value?

AGI Focus — Keeping the long-term mission in view while shipping today.

  • “Why do you want to work on AI specifically?”
  • “How do you think about the societal impact of the systems you build?”
  • “What excites you and what concerns you about the current state of AI?”

Intensity and Ownership — Operating with urgency on hard problems.

  • “Tell me about a time you went significantly above and beyond to ship something.”
  • “Describe a project where you took full ownership from start to finish.”
  • “How do you handle working under pressure on high-stakes deliverables?”

Intellectual Rigor — Thinking carefully and precisely about complex problems.

  • “Tell me about a time you had to reason through a problem with incomplete information.”
  • “Describe a technical decision where you had to weigh competing trade-offs.”
  • “How do you distinguish between a good enough solution and the right solution?”

Collaborative Excellence — Working with world-class researchers and engineers.

  • “Tell me about a time you worked with someone much more experienced than you. What did you learn?”
  • “Describe a situation where you had to bridge the gap between research and production.”
  • “How do you handle disagreements about technical direction?”

Safety Awareness — Thinking proactively about risks and mitigations.

  • “How do you think about failure modes in the systems you build?”
  • “Tell me about a time you identified a risk that others hadn’t considered.”
  • “What does ‘safe by default’ mean to you in engineering?”

Questions You Should Be Ready to Discuss

  • Your understanding of OpenAI’s mission and how your role contributes
  • Your perspective on the current capabilities and limitations of AI
  • How you think about deploying AI systems responsibly
  • A time you had to make a difficult trade-off between speed and safety/quality
  • What “building AGI that benefits all of humanity” means in practical terms
  • Your familiarity with OpenAI’s products (ChatGPT, API, GPTs, Assistants, etc.)

How to Prepare

  1. Use the OpenAI API — Build something with it so you can discuss it from experience
  2. Read OpenAI’s research blog — Understand their approach to scaling, safety, and alignment
  3. Study AI infrastructure patterns — Model serving, GPU clusters, data pipelines
  4. Practice coding under time pressure — Clean, correct solutions in 20-25 minutes per problem
  5. Develop genuine perspectives on AI — These culture rounds reward authentic, thoughtful answers, not rehearsed talking points
  6. Use the STAR method for behavioral stories: Situation, Task, Action, Result

On this page