OpenAI System Design Interview Questions: Complete Preparation Guide

Reading time: 12 minutes
Best for: Software engineers preparing for OpenAI's system design round

This guide covers what the OpenAI system design interview tends to feel like, how to handle their interview style, and the kinds of OpenAI system design interview questions candidates commonly encounter.

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The biggest gotchas: What makes OpenAI different

Gotcha 1: System design can appear in both phone screen AND onsite

Some candidates have reported getting a system design round during the phone screen, and then another one during the onsite. This varies by team, and not everyone gets this format, but it's common enough that you should be mentally prepared for it. If you're asked to do system design twice, the second round may go deeper or test different aspects of your thinking.

Gotcha 2: You may need to think beyond backend infrastructure

This is critical: At OpenAI, backend-only thinking might not be enough. Some of their design tasks probe whether you can think about how the product actually behaves from the user's point of view.

What that can look like in practice:

Streaming UX: If you're designing something ChatGPT-like, do responses stream token-by-token or appear all at once? What happens in the UI if the stream stalls, errors, or gets interrupted? How does the client recover if the connection drops midway?

Optimistic vs confirmed UI states: In a messaging or webhook-heavy product, when does the UI show something as "sent," "delivered," "failed," or "retrying"? If you're building Slack-like messaging, do you show a message instantly and reconcile later, or wait for server confirmation?

Real-time collaboration: If multiple users are interacting with the same system, how is the UI updated? Do users see typing indicators, live status changes, workflow state transitions, or partial progress updates? What happens when two users trigger conflicting actions at almost the same time?

Latency hiding: If an operation is expensive, what does the user see while waiting? Do you use skeleton states, staged loading, incremental rendering, or partial results? In an AI product, can you return a draft answer early and improve it, or do you block until the full pipeline completes?

Failure handling from the user's perspective: A lot of backend candidates stop at "the service retries." But product-aware design means asking: what does the user actually see when retries are happening? Does the interface expose errors clearly, silently recover, allow replay, or provide a fallback action?

The broader point is this: don't assume OpenAI system design is just about server-side plumbing. Some of their design tasks reward candidates who can think about how architecture decisions actually show up in the product experience.

Gotcha 3: Real-time information processing and cognitive flexibility is especially important

A lot of candidates prepare system design by following a familiar sequence: requirements, rough numbers, API, data model, high-level design, deep dive, scaling, done. That approach can work in many interviews. But one of the OpenAI gotchas is that the interviewer may throw in new constraints, new product requirements, or a twist halfway through.

So the interview isn't just testing whether you can produce a design. It's testing whether you can process new information in real time and adapt without losing the thread.

That means:

• You can't rely too heavily on memorized templates
• You need to actually understand the design deeply enough to reshape it on the fly
• You need to stay mentally flexible when the interviewer changes the target

The skill being tested is not just design knowledge. It's cognitive flexibility under pressure.

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What makes OpenAI's system design interview unique?

If you name-drop a technology, be ready to defend it

A lot of people, especially in system design, casually say things like "I'd use Kafka here," "I'd put this in DynamoDB," "Redis for this," or "Let's use Postgres." At OpenAI, based on candidate reports, that can be dangerous if you're saying it by reflex rather than by reasoning.

The safer way to think about it is: if you mention a technology, assume you may be asked to justify why it fits this exact context.

So if you say Kafka, you should be ready to explain why an event log is useful here, why asynchronous processing is appropriate, what ordering guarantees matter, what the retry story is, whether duplicate delivery is acceptable, and whether a simpler queue would have been enough.

If you say DynamoDB, you should be ready to explain why key-value access patterns fit the workload, what your partition key design is, why you don't need richer relational queries, what consistency tradeoff you're accepting, and whether hot partitions could become a problem.

If you say Redis, you should be ready to explain why memory-backed low-latency access is worth the tradeoff, what happens on failure, whether it's a cache, a lock store, a rate limiter, or ephemeral state holder, and why persistence is or isn't important.

The real advice here is: don't tech-name-drop for style points. Mention a technology only if you can defend why it's useful in that specific design.

Design tasks often feel more "can you build useful systems?" than "design a famous app"

Yes, there are some classic-feeling design tasks in the mix, like Netflix or ChatGPT or online chess. But a lot of the more interesting ones feel very build-oriented:

• Payments pipelines
• Webhook delivery
• Webhook callbacks
• GitHub Actions
• Multi-tenant CI/CD
• Slack-like team messaging MVPs

These aren't just "scale this giant famous company" tasks. They feel more like: can you reason about the kind of systems a strong builder would have to create in real life? That includes systems that involve workflow orchestration, retries and failure handling, async processing, concurrency, developer tooling, integrations, state transitions, user-facing operational status, and minimum viable launches, not just fully mature hyperscale systems.

A significant number of design tasks start with MVP thinking, then scale later

Not all of them, but enough that you should be ready for it. A lot of candidates enter system design interviews assuming they should immediately jump to the final fully scaled architecture: global sharding, distributed queues, geo-replication, fanout infra, sophisticated caching layers, and so on.

But with some OpenAI-style design tasks, that may not be the best opening move.

A better framing is: first, show that you can build a working version, then show that you can evolve it as load, complexity, or product requirements increase. So instead of immediately designing the most elaborate system possible, you might need to:

• Define a realistic MVP
• Identify the core entities and flows
• Make the simplest version work
• Then respond as the interviewer adds scale, reliability, tenant isolation, throughput, or product complexity

That means preparation should include MVP-first design thinking, incremental evolution, knowing when to keep things simple, and knowing when to introduce more advanced infra.

Don't assume the goal is to dump a hyperscale architecture immediately. Sometimes the real test is whether you can start practical and evolve intelligently.

Memorizing polished "model solutions" is not enough

Deep understanding and adaptability matter more. If the interviewer changes constraints mid-way, asks you to justify each technology choice, starts from an MVP and pushes toward scale later, or expects product-aware reasoning and not just backend reasoning, then memorizing canned system design answers becomes much less useful.

You can still study patterns, of course. You should absolutely know the common building blocks. But the goal is not to recite a prefab answer. The goal is to understand the design space well enough to reshape it in real time.

At OpenAI, the winning strategy is not "memorize solutions." It's "understand the design space well enough to adapt when the interviewer moves the goalposts."

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Past OpenAI system design questions

These are real system design questions that candidates have reported facing in OpenAI interviews, grouped by category:

Payments and money movement

Design a payments pipeline
Forward payments to a payment processor, hold funds, then batch-settle daily. Tests whether you can reason about workflows, correctness, failure handling, reconciliation, and staged processing.

Webhooks and third-party integrations

Design a webhook callback system for third-party integrations
Design a webhook delivery platform
Tests async processing, delivery guarantees, retries, idempotency, failure handling, and external-system uncertainty. Highly practical and "real builder" oriented.

CI/CD and developer workflows

Design a multi-tenant CI/CD workflow system for many orgs
Design GitHub Actions from scratch
Tests workflow engines, job scheduling, state management, multi-tenancy, isolation, and execution pipelines. These probe whether you can reason about developer tooling and orchestration systems.

Real-time interaction and concurrency

Design online chess
Design a Slack-like team messaging service
Tests state synchronization, concurrency, latency, live updates, and user-facing consistency. These systems force you to think about real-time behavior, user experience, state conflicts, and responsiveness.

For a complete walkthrough of the Slack MVP design task, see this GitHub gist which covers requirements gathering, API design, architecture, and key technical decisions.

Big product systems

Design Netflix
Design ChatGPT
These may look like classic "design X" tasks, but in this context, they may still be used to test practical reasoning, product awareness, and system evolution.

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Recommended framework for OpenAI system design interviews

This framework gives you a solid structure while remaining flexible enough to handle OpenAI's interview style.

Step 1: Understand and confirm the goal (2-3 minutes)

As the interviewer presents the problem (however ambiguous) write it down as they speak. Then repeat it back to confirm understanding.

Template: "So we are designing [system] that [core functionality], optimizing for [key constraint]."

Example: "So we are designing a webhook delivery platform that handles third-party callbacks reliably at scale, optimizing for delivery guarantees and abuse resistance."

Even if the problem is vague, state what you understand and get confirmation before proceeding.

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Step 2: Gather requirements (10-15 minutes)

This phase has two parts: functional requirements and non-functional requirements.

Functional requirements

Break down features into three priority tiers:

• Must-do: Core functionality you'll definitely design for in detail
• Should-do: Important features you'll acknowledge and touch on, but may not detail fully given time constraints
• Nice-to-do: Features you'll mention if time permits

State your assumptions explicitly and get sign-off from the interviewer that your understanding is correct.

Example:

• Must-do: "System accepts webhook subscriptions and delivers events to registered URLs"
• Should-do: "System provides delivery confirmation and retry history"
• Nice-to-do: "System supports webhook signature verification"

Non-functional requirements

Cover these systematically, being quantitatively and qualitatively precise:

Scale:

• Events per second: Specify expected load (e.g., "100K events/second at peak")
• Subscribers: Define number of webhook endpoints (e.g., "10K active subscribers")
• Growth rate: Clarify growth expectations (e.g., "growing 2× annually")

Performance:

• Latency: Define target delivery time (e.g., "p99 delivery within 1 second of event creation")
• Throughput: Specify processing capacity needed (e.g., "must handle 100K events/second")

Reliability:

• Delivery guarantees: Define semantics (e.g., "at-least-once delivery, idempotency key provided")
• Retry strategy: Specify behavior (e.g., "exponential backoff, max 5 retries over 24 hours")
• Dead letter handling: What happens to undeliverable events (e.g., "store for 7 days, alert subscriber")

Availability and fault tolerance:

• Uptime target: Define acceptable downtime (e.g., "99.9% uptime means ~43 minutes downtime per month")
• Degraded mode: Specify behavior when dependencies fail (e.g., "queue events if delivery fails, process when recovered")

Critical: Be precise with numbers. Don't just say "low latency" but rather say "p99 delivery within 1 second." Don't just say "high availability" but rather say "99.9% uptime or ~43 minutes downtime per month."

Asking good questions vs. bad questions

Bad questions (you should infer these yourself):

• "Should we use microservices?"
• "Should this be scalable?"
• "What technology stack should I use?"

These signal you want to be spoon-fed. You're the designer. Demonstrate judgment.

Good questions (show insight and initiative):

• "Based on 100K events/second, I'm estimating we'll need ~50 worker instances at peak. Does that scale sound right, or should I plan for different capacity?"
• "Given this delivery model, we could have duplicate deliveries if a subscriber's server is slow to respond. I'm planning to provide idempotency keys. Any objections?"
• "I'm thinking p99 delivery within 1 second is acceptable for this use case. Is that aligned with subscriber expectations, or do we need tighter SLAs?"

Good questions demonstrate you're:

1. Using your judgment and experience
2. Inferring reasonable answers
3. Taking the driver's seat rather than being passive

Get explicit sign-off: After gathering requirements, confirm: "Based on what we've discussed, here's my understanding of priorities and constraints: [summarize]. Does this match your expectations?"

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Step 3: Design the data model and APIs (< 10 minutes)

Define the core entities, relationships, and API surface area.

Data model

Identify primary entities and how they relate:

Example for a webhook delivery platform:

Entities:
- Subscription (id, subscriber_id, url, event_types[], created_at, active)
- Event (id, event_type, payload, created_at, status)
- Delivery (id, event_id, subscription_id, attempt_count, status, next_retry_at)
- DeliveryLog (id, delivery_id, attempted_at, response_code, response_body)

Key indexes:
- event_type on Event for filtering subscriptions
- status on Delivery for finding pending/failed deliveries
- next_retry_at on Delivery for retry queue
- subscription_id on Delivery for subscriber-specific queries

Partition key: event_id (enables horizontal scaling, each event's deliveries stored together)

API design

Define the core endpoints - the essential APIs your system needs. If you find yourself designing 10+ endpoints, you're probably overdoing it. Focus on the critical flows.

Example RESTful API:

POST /api/v1/subscriptions
  - Create new webhook subscription
  - Body: { url, event_types: ["order.created", "order.shipped"] }
  - Returns: { subscription_id, url, secret }

POST /api/v1/events
  - Publish new event (internal)
  - Body: { event_type, payload, idempotency_key }
  - Returns: { event_id, queued_deliveries: 5 }

GET /api/v1/subscriptions/:subscription_id/deliveries
  - Get delivery history for a subscription
  - Query params: ?status=failed&limit=20
  - Returns: paginated list of delivery attempts

POST /api/v1/deliveries/:delivery_id/retry
  - Manually retry failed delivery
  - Returns: { delivery_id, status: "retrying", scheduled_at }

Be specific about idempotency, error handling, and retry behavior.

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Step 4: Present the high-level design (25 minutes)

This is your main design phase. Present the architecture, justify decisions, and iterate.

Start with the baseline flow

Show the simplest version that works:

Example:

Publisher
  → API Gateway (auth, rate limiting)
    → Event Service (validation, persistence)
      → Event Queue (for async delivery)
        → Delivery Workers (fetch events, call webhooks)
          → Subscriber URLs

Perform back-of-the-envelope calculations (when relevant)

Do this when: Scale, capacity, or cost are critical constraints requiring numerical justification (e.g., "How many workers?", "What's the queue depth?", "What's the bandwidth requirement?").

Skip this if: The problem doesn't require capacity planning, or you can address scale conceptually during the design discussion.

Example where calculations matter:

• 100K events/second at peak
• 10K active subscriptions
• Average fanout: 5 subscribers per event
• Peak webhook calls: 100K × 5 = 500K calls/second
• Average delivery time: 200ms
• Workers needed: 500K × 0.2s = 100K concurrent deliveries → ~1000 workers (assuming 100 concurrent deliveries per worker)

This justifies choosing asynchronous processing and a worker pool architecture.

Layer in constraint-driven upgrades

Only add complexity when requirements force it. Justify each addition:

Scale upgrades (only if scale requires them):

• Sharding: "We'll partition events by event_id across 8 shards to distribute load"
• Queue partitioning: "Each shard has its own queue to prevent head-of-line blocking"
• Worker pools: "Separate worker pools per priority tier (critical, standard, bulk)"

Reliability upgrades:

• Idempotency: "Every event includes an idempotency key. Subscribers can deduplicate using this"
• Retries: "Exponential backoff: 1s, 2s, 4s, 8s, 16s, up to 5 retries over 24 hours"
• Circuit breakers: "Open after 10 consecutive failures to a subscriber, prevent wasted retries"
• Timeouts: "30-second timeout per delivery attempt to prevent slow subscribers from blocking workers"

Operational upgrades:

• Dead letter queue: "Events that exhaust retries move to DLQ, retained 7 days for manual investigation"
• Delivery confirmation: "Subscribers receive webhook with signature, must return 2xx within 30s to confirm"
• Rate limiting: "Per-subscriber rate limit of 100 events/second to prevent overwhelm"

Justify every decision as you go

Don't just draw boxes. Explain why, including what you're trading off:

Example 1: Queue vs. synchronous delivery
"We use a queue here because delivery is slow (200ms average external HTTP call) and we need to decouple event creation from delivery. The alternative is synchronous processing, which would give us simpler code and immediate confirmation, but would block the API on external subscriber performance. Since we need 100K events/second, we accept the complexity of a queue to scale horizontally and isolate failures."

Example 2: At-least-once vs. exactly-once
"We chose at-least-once delivery because it's simpler to implement and most subscribers can handle duplicates via idempotency keys. Exactly-once would require distributed transactions across our system and subscriber systems, adding significant complexity and latency. For this use case, duplicate tolerance is acceptable, so we optimize for reliability and throughput."

Example 3: Retry strategy
"We use exponential backoff because it gives temporary issues time to resolve while preventing thundering herd. The alternative is fixed-interval retries, which would be simpler but could overwhelm a recovering subscriber. We cap at 5 retries over 24 hours because after that, the issue is likely persistent and requires human intervention."

Recommended time allocation:

• Baseline flow: 5 minutes
• Back-of-the-envelope: 5 minutes (if needed)
• Layering in upgrades: 10 minutes
• Justifying decisions: 5 minutes (ongoing as you design)

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Step 5: Address failure modes and observability

As you present your design, proactively mention failure modes at a high level.

Mention key failure scenarios briefly

Identify 2-3 critical failure modes and state how you'd handle them:

Example 1: Queue depth grows unbounded
"If deliveries slow down and queue depth exceeds threshold, we apply backpressure at the API, returning 429 for new events with Retry-After header. We also scale workers horizontally based on queue depth metrics."

Example 2: Subscriber endpoint is down
"Circuit breaker opens after 10 consecutive failures. We stop attempting deliveries to that endpoint, log the circuit open event, and alert the subscriber. Circuit stays open for 5 minutes, then enters half-open state to test recovery."

Example 3: Worker crashes mid-delivery
"Deliveries are marked 'in-progress' before sending. If worker crashes, delivery remains in-progress. A separate cleanup job finds stale in-progress deliveries (older than 5 minutes) and requeues them. This ensures at-least-once delivery."

Define observability clearly

Metrics to track:

• Delivery latency: p50, p95, p99 by subscriber (alert if p99 > 5 seconds)
• Delivery success rate: by subscriber and event type (alert if < 95%)
• Queue depth: by priority tier (alert at 80% capacity)
• Retry rate: percentage of deliveries requiring retries (alert if > 20%)
• Circuit breaker status: number of open circuits (alert on any open)

Dashboards: Real-time view of delivery health, queue depth, subscriber health
Alerts: Automated notifications to on-call for threshold violations
Logs: Structured delivery logs with event_id, subscriber_id, attempt_count, response_code, duration

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Step 6: Deep dive (15 minutes)

The interviewer will likely push you into specific areas. Be ready to go 2-3 levels deeper on any decision.

Best practice: Present a menu of options to the interviewer. This demonstrates initiative and allows you to discover what they care about.

Example opening:
"I can dive deeper into several areas: (1) how we prevent duplicate deliveries and handle idempotency, (2) our retry and circuit breaker strategy, or (3) how we handle webhook signature verification. Which would be most valuable to explore?"

Common deep dive areas

Problem-specific walkthroughs (most common):

• How you handle delivery confirmation and retries
• How you prevent overwhelming slow subscribers
• How you ensure ordering for events from the same source
• How you handle webhook signature verification

Generic system design topics (possible):

• Race conditions and correctness guarantees
• Fault tolerance and recovery procedures
• Scale and performance under load
• Security and abuse prevention

How to handle deep dives

Go 2-3 levels deep with specifics:

Example: Idempotency in webhook delivery

Level 1 (surface): "We include an idempotency key with each webhook delivery"

Level 2 (mechanism): "Each event has a unique event_id. We include this in the webhook payload and in a custom header X-Webhook-Id. Subscribers can use this to deduplicate if they receive the same event multiple times due to retries."

Level 3 (edge cases and tradeoffs): "If a subscriber's server crashes after receiving the webhook but before processing it, they might request a replay. We provide an API endpoint to manually trigger redelivery using the original event_id, ensuring subscribers get the exact same payload. The tradeoff is subscribers must implement idempotent handling, which we document in our webhook integration guide. We also provide signature verification to prevent replay attacks from malicious actors."

Show tradeoffs: No decision is perfect. Acknowledge downsides.

Use examples: Walk through concrete scenarios.

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Recommended preparation approach

System design preparation isn't about cramming in 7 days but rather about building judgment through progressive phases.

Phase 1: Foundational knowledge (prerequisite)

Before attempting interview problems, solidify core systems concepts:

Essential topics to understand:

• Scalability patterns: caching, sharding, replication, load balancing
• Data consistency models: strong vs. eventual consistency, CAP theorem tradeoffs
• Reliability patterns: retries, circuit breakers, timeouts, backpressure
• Common architectures: monolith vs. microservices, event-driven systems, message queues
• Storage fundamentals: relational vs. NoSQL, when to use each, indexing strategies

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Phase 2: Interview learning phase (expect to struggle)

Now attempt interview-style problems with a learning mindset, not performance pressure.

What this phase looks like:

• Pick system design problems (use the actual OpenAI questions in this guide)
• Try to solve them, but expect to get stuck frequently
• When you hit gaps in knowledge, stop and research
• Look up solutions after attempting, compare to your approach
• Focus on understanding why certain decisions were made

How to practice:

• Spend 1-4 hours attempting a problem
• Identify where you got stuck or made poor choices
• Research those specific topics
• Review solutions
• Repeat with a different problem

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Phase 3: Interview training phase (timed practice)

Once you're comfortable with the patterns, shift to performance mode.

What this phase looks like:

• Solve problems under real time pressure (60 minutes)
• Mix problems you've seen before with completely new ones
• Focus on execution: Can you articulate decisions clearly? Justify tradeoffs? Handle ambiguity?
• Self-evaluate: Would you hire yourself based on this performance?

Practice structure:

• Set a 60-minute timer
• Write or draw your design (simulating whiteboard/doc)
• Speak out loud as if explaining to an interviewer
• Record yourself to identify rambling or unclear reasoning

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Phase 4: Mock interview phase (realistic simulation)

The final phase introduces the human element and realistic interview pressure.

What this phase looks like:

• Practice with another person acting as interviewer, preferably an experienced interviewer
• Get feedback on: communication clarity, technical depth, tradeoff reasoning, handling pressure, and overall hire/no-hire assessment
• Professional mock interviews provide the most valuable feedback with calibrated assessments

Why this matters: Real interviews aren't just about knowing the answer but rather about:

• Engaging with the interviewer while problem-solving
• Handling unexpected questions mid-design
• Staying calm when challenged
• Driving the conversation (or adapting when the interviewer drives)

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Frequently asked questions

Q: How much time should I spend on each phase of the interview?
A: Use this as a guideline:

• Requirements gathering: 10-15 minutes
• Data model & APIs: < 10 minutes
• High-level design: 25 minutes
• Deep dive: 15 minutes

Be flexible if the interviewer redirects you, but this allocation ensures you cover all critical areas.

Q: What if the interviewer changes requirements mid-interview?
A: This is common at OpenAI and tests cognitive flexibility. Stay calm, summarize the new constraint, and explain how it changes your design. Show you can adapt rather than rigidly following a memorized solution.

Q: Should I mention specific technologies or keep it generic?
A: Mention specific technologies only if you can justify why they fit this exact context. Be ready to defend your choice and explain alternatives. Don't name-drop for style points.

Q: How do I know if I should start with MVP or full-scale architecture?
A: Listen to the problem statement. If it mentions "MVP," "2-week launch," "small team," or "v1," start simple and evolve. If it mentions "billion users," "global scale," or "mature product," you can start with more sophisticated architecture. When in doubt, ask.

Q: What's the best way to handle ambiguity in the problem statement?
A: Ask clarifying questions, but don't overdo it. Make reasonable assumptions, state them explicitly, and get sign-off. Show you can operate under uncertainty rather than being paralyzed by it.