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Frontier AI labs · International · Loop reviewed 2026-09-26

Guides describe practical coding rather than puzzles, a project presentation and, in some loops, an AI-assisted coding round. Product managers are expected to work close to general managers. The official interview guide could not be read for this edition, so every stage below is secondary.

On this page: Interview loop · Questions (31)

Roles covered

• AI Engineering: Software engineer (general interview baseline), AI / LLM engineer, Forward deployed engineer (FDE)
• AI Leadership: Product manager (general interview baseline)

Interview loop

Stage	What happens	Basis
Recruiter call	About 30 minutes on background, motivation and fit for the role.	† prep guide or compilation OpenAI's Interview Process & Questions, interviewing.io, retrieved 2026-09-26 OpenAI Product Manager (PM) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26
Technical screens	About an hour of practical coding, and for engineers a separate hour-long system design screen.	† prep guide or compilation OpenAI's Interview Process & Questions, interviewing.io, retrieved 2026-09-26
Engineering onsite	Four to six hours: coding, system design, a project presentation with slides, a behavioral conversation with a senior manager that covers AI safety, and a collaboration round. An agentic, AI-assisted coding round on an existing codebase was in beta.	† prep guide or compilation OpenAI's Interview Process & Questions, interviewing.io, retrieved 2026-09-26
Product manager loop	A hiring manager screen, an hour-long product sense screen and a product execution screen, then four to six final rounds across product sense, execution, go-to-market, engineering partnership, stakeholders and behavioral questions.	† prep guide or compilation OpenAI Product Manager (PM) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26
Forward deployed engineer loop Forward deployed engineer (FDE)	A guide describes a process that varies by team: a recruiter screen, early coding and design screens or a take-home with review, followed by a virtual onsite. It estimates three to four weeks overall.	† prep guide or compilation OpenAI Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-29

Coding: unknown. Secondary guides describe coding; no company or first-hand source verifies the requirement in this edition. (OpenAI's Interview Process & Questions, interviewing.io, retrieved 2026-09-26, OpenAI Product Manager (PM) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26)

Questions reported for OpenAI

LLM fundamentals

• Derive the memory required by a decoder's KV cache as batch size and context length grow.
  • Knowledge · Asked at: Amazon †, OpenAI † · ✍ Answer
  • Tests: Whether cache size is calculated from the actual architecture.
  • A strong answer covers:
    • For equal sequence lengths, use 2 × batch × layers × tokens × KV heads × head width × bytes.
    • Explain why queries are not retained for future decode steps.
    • Account separately for weights, activations, padding and allocation overhead.
  • Read: Efficient Memory Management for Large Language Model Serving with PagedAttention (Kwon et al., arXiv) · GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., arXiv)
• Relate cross-entropy, KL divergence and perplexity for next-token prediction; state the assumptions behind each comparison.
  • Knowledge · Asked at: OpenAI †
  • Tests: Whether probabilistic definitions are used consistently.
  • A strong answer covers:
    • Show cross-entropy equals data entropy plus KL from data to model, so for fixed data minimising either is equivalent.
    • Define perplexity as exp of mean per-token cross-entropy in nats; it shifts with tokeniser, eval text and context length.
    • Compare across tokenisers in bits per byte, fixing the text, context handling and log base before drawing conclusions.
  • Read: Deep Learning: Probability and Information Theory (Ian Goodfellow, Yoshua Bengio and Aaron Courville)
• Explain how self-attention cost grows with context length and compare ways to reduce that cost.
  • Knowledge · Asked at: OpenAI †
  • Tests: Whether efficiency changes are distinguished from changes to model behaviour.
  • A strong answer covers:
    • Show the cost: prefill score and value products scale with T squared; each decode step reads a KV cache linear in T.
    • Separate exact kernels like FlashAttention, which cut memory traffic, from windows, sparsity, KV eviction or GQA, which change outputs.
    • Re-run long-context quality evals for behaviour-changing methods; for exact ones, check numerical equivalence and speed only.
  • Read: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (Dao et al., arXiv) · GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., arXiv)

Inference, serving and cost

• After a deployment, p99 latency doubles although model weights are unchanged. How do you isolate the cause?
  • Applied scenario · Asked at: Amazon †, Databricks †, OpenAI †, Perplexity † · ✍ Answer
  • Tests: Whether diagnosis separates queueing, scheduling, dependencies and traffic changes.
  • A strong answer covers:
    • Compare traffic mix, queue time and per-stage latency before and after release.
    • Inspect cache hit rates, batching, dependency tails and resource saturation.
    • Reproduce one hypothesis at a time and use a rollback or canary to verify causality.
  • Read: The Tail at Scale (Dean and Barroso, Google Research)

Agents, tools and protocols

• Design recovery from tool errors and timeouts, including cases where a timed-out call may already have changed external state.
  • System design · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether retries preserve correctness as well as availability.
  • A strong answer covers:
    • Classify transient, permanent and ambiguous outcomes before retrying.
    • Use idempotency keys or reconcile external state after uncertain writes.
    • Bound attempts and deadlines; add backoff, jitter and an escalation path.
  • Read: Designing robust and predictable APIs with idempotency (Stripe)
• Design human approval for consequential agent actions, including how approval remains bound to the exact action being executed.
  • System design · Asked at: OpenAI †, Palantir † · ✍ Answer
  • Tests: Whether approval controls are enforced outside the model.
  • A strong answer covers:
    • Preview the concrete action, target and consequences before approval.
    • Bind approval to validated arguments and recheck authorisation at execution.
    • Record the decision and execution result; expire approval after material changes.
  • Read: Building Effective AI Agents (Anthropic) · Designing robust and predictable APIs with idempotency (Stripe)

Fine-tuning and post-training

• Choose between prompt changes, retrieval and finetuning for a failing AI feature; include data freshness, latency and total cost.
  • Applied scenario · Asked at: Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether the intervention matches the observed failure mode.
  • A strong answer covers:
    • Classify missing knowledge, poor instructions and unwanted behaviour separately.
    • Establish a prompt baseline; test retrieval for fresh attributable knowledge.
    • Use finetuning when representative examples justify it, then compare lifecycle cost and held-out quality.
  • Read: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., arXiv) · LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., arXiv)

Evaluation and observability

• How would you measure unsupported claims in a deployed RAG application without treating every fluent answer as correct?
  • System design · Asked at: Anthropic †, OpenAI † · ✍ Answer
  • Tests: Whether faithfulness to evidence is separated from fluency and general plausibility.
  • A strong answer covers:
    • Make the unit a claim checked against the passages actually retrieved for that answer.
    • Scale labelling with a judge calibrated against a human-labelled sample.
    • Separate a retrieval failure from an unfaithful generation when attributing the error.
  • Read: Your AI Product Needs Evals (Hamel Husain)
• A customer reports worse answers after a model upgrade. How do you verify the regression and decide what to restore or change?
  • Applied scenario · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether concrete customer failures drive reproduction and remediation.
  • A strong answer covers:
    • Collect the customer's exact failing requests and list every change shipped with the upgrade, not only the model.
    • Replay old and new configurations on identical inputs with repeated samples and blind side-by-side grading.
    • Choose the remedy by cause: fix configuration, adapt the prompt or pin the version; add cases to the release gate.
  • Read: Your AI Product Needs Evals (Hamel Husain)

Safety, security and governance

• How would you approach safety for a generative-AI consumer product?
  • Product case · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether safety requirements reflect actual users and plausible harm.
  • A strong answer covers:
    • Choose a concrete use case, user population and abuse model.
    • Combine product limits, model evaluation, reporting and human escalation.
    • Measure both harmful completions and unnecessary refusals by user segment.
  • Read: AI Risk Management Framework (NIST) · OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project)
• What safeguards should surround an AI system that acts on a user's behalf?
  • System design · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you distinguish generated intent from authorised execution.
  • A strong answer covers:
    • Scope credentials to the user, resource and operation.
    • Show consequential actions for confirmation and enforce policy at execution.
    • Record actions, bound spending and provide recovery for partial completion.
  • Read: OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project) · Designing robust and predictable APIs with idempotency (Stripe)

AI system design

• Design a knowledge assistant over ten million enterprise documents with per-user permissions and a continuously changing corpus.
  • System design · Asked at: Amazon †, Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether ingestion, retrieval, access control and serving form an operable system.
  • A strong answer covers:
    • Clarify query volume, freshness and permission semantics before sizing.
    • Separate ingestion and index versions from query-time authorisation and retrieval.
    • Evaluate grounded answers and test revocation, stale data and partial outages.
  • Read: Document-level access control (Microsoft Learn) · Contextual Retrieval in AI Systems (Anthropic)
• Design a streaming chat service for hundreds of millions of users, including capacity, conversation storage and graceful overload behaviour.
  • System design · Asked at: Anthropic †, Google and Google DeepMind †, Meta †, OpenAI † · ✍ Answer
  • Tests: Whether scale assumptions lead to explicit capacity and reliability decisions.
  • A strong answer covers:
    • Estimate concurrent streams, token demand and storage from declared traffic assumptions.
    • Separate admission control, inference scheduling and durable conversation state.
    • Plan capacity headroom, cancellation, overload rejection and recovery from partial failure.
  • Read: Site Reliability Engineering (Google) · Efficient Memory Management for Large Language Model Serving with PagedAttention (Kwon et al., arXiv)
• Design reliable delivery of events to customer webhook endpoints despite timeouts, duplicate attempts and unavailable receivers.
  • System design · Asked at: OpenAI †
  • Tests: Whether delivery guarantees and consumer responsibilities are stated precisely.
  • A strong answer covers:
    • State the guarantee as at-least-once and unordered, and require consumers to deduplicate by a stable event id.
    • Write events to a durable outbox in the same transaction, then retry with capped backoff and jitter.
    • Isolate failing endpoints in per-endpoint queues, disable after a retry window, and support replay.
  • Read: Designing robust and predictable APIs with idempotency (Stripe) · Standard Webhooks specification (Standard Webhooks)

Practical coding

• Build a cache with constant-time lookup and recency updates, then add expiry without breaking eviction semantics.
  • Coding · Asked at: OpenAI †
  • Tests: Whether data-structure invariants survive an evolving specification.
  • A strong answer covers:
    • Clarify capacity, whether reads refresh recency, and whether expired entries still count until purged.
    • Use a hash map with a doubly linked list for O(1); one removal path serves expiry and eviction.
    • Test capacity one, updating an existing key, and expiry at exact boundaries with an injected clock.
  • Read: collections: OrderedDict objects (Python Software Foundation) · heapq: Heap queue algorithm (Python Software Foundation)
• Implement a token-bucket limiter and explain what must change when several workers enforce the same limit.
  • Coding · Asked at: Anthropic †, OpenAI † · ✍ Answer
  • Tests: Whether time accounting and concurrent updates preserve the quota.
  • A strong answer covers:
    • Refill from elapsed monotonic time and cap tokens at capacity.
    • Make the check-and-consume operation atomic across competing calls.
    • Test bursts and time boundaries; state the shared-store or partitioning contract for distributed enforcement.
  • Read: Scaling your API with rate limiters (Stripe)
• Implement an in-memory key-value store with begin, commit and abort, stating the transaction semantics you support.
  • Coding · Asked at: OpenAI †
  • Tests: Whether rollback and visibility match a precise contract.
  • A strong answer covers:
    • State the contract: whether transactions nest, and whether other clients can see uncommitted writes.
    • Keep a stack of write layers with deletion markers; reads search top-down, commit merges into the parent.
    • Test deleting a key set in the same transaction, nested abort, and commit or abort with none open.
  • Read: Transaction Isolation (PostgreSQL documentation)
• Refactor a small, poorly structured program with passing tests. Explain the first change and how you preserve observable behaviour.
  • Coding · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether improvements are incremental and justified by maintainability.
  • A strong answer covers:
    • Pin down observable behaviour first, adding characterisation tests where existing tests assert too little.
    • Make the first change small and mechanical, such as extracting a function, running tests after each step.
    • Justify each step by a maintenance cost it removes, and never mix refactoring with behaviour changes.
  • Read: Working Inside Existing Systems (Palantir Careers) · Refactoring (Martin Fowler)
• Build a semantic search over a product catalogue that a chat model can call as a tool.
  • Coding · Asked at: OpenAI †
  • Tests: Whether you combine retrieval with a clear, validated tool interface that a model can use reliably.
  • A strong answer covers:
    • Define the tool contract: query, typed filters, a result limit, and compact results with stable product ids.
    • Index product text with embeddings plus keyword matching, and apply filters before ranking.
    • Return actionable errors and explicit empty results; evaluate tool selection and arguments on realistic chat requests.
  • Read: Writing effective tools for AI agents—using AI agents (Anthropic) · Tool use with Claude (Claude Platform Docs)
• Implement a credit system in which credits carry different expiry rules and usage requirements, and extend it as follow-up requirements arrive.
  • Coding · Asked at: OpenAI †
  • Tests: Whether your design absorbs changing business rules without a rewrite.
  • A strong answer covers:
    • Model credits as grants with amount, expiry and conditions, and usage as append-only ledger entries.
    • Isolate the consumption policy, such as soonest expiry first via a heap, so new rules plug in.
    • Test expiry at the exact timestamp, consumption spanning grants, insufficient balance and rebuilding balances from the ledger.
  • Read: heapq: Heap queue algorithm (Python Software Foundation) · Transaction Isolation (PostgreSQL documentation)

AI product strategy and metrics

• How would you improve ChatGPT for enterprise customers?
  • Product case · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you select a specific workflow and justify its business value.
  • A strong answer covers:
    • Choose a customer segment, user and costly workflow problem.
    • Compare AI and non-AI solutions, integration effort and trust requirements.
    • Define a pilot outcome, quality guardrails and a rollout decision.
  • Read: Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research) · Rules of Machine Learning (Google for Developers)
• A model offers much greater capability but costs ten times as much. How would you decide what product to build with it?
  • Product case · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you translate capability into willingness to pay and unit economics.
  • A strong answer covers:
    • Identify tasks where the improvement changes the feasible outcome.
    • Estimate cost per successful workflow, including retries and human review.
    • Test willingness to pay and consider selective routing before broad deployment.
  • Read: Rules of Machine Learning (Google for Developers) · Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research)
• You have invented a memory machine that generates video, images, smells and sounds. How would you bring it to market?
  • Product case · Senior · Asked at: OpenAI †
  • Tests: Whether you turn an unfamiliar capability into a focused market entry.
  • A strong answer covers:
    • Choose one beachhead user and occasion, and only the senses that occasion actually needs.
    • Measure repeat paid use, with guardrails on user distress and the consent of people depicted.
    • Test willingness to pay with a hand-run concierge pilot before building scent hardware.
  • Read: User Needs + Defining Success (Google PAIR) · How the alpha phase works (Government Digital Service)

Programmes and delivery

• Walk through the most difficult product launch you led.
  • Behavioral · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you can explain dependencies, decisions and your contribution to the result.
  • A strong answer covers:
    • Set out the goal, constraints, stakeholders and critical dependencies.
    • Explain a consequential decision, alternatives and how you kept people aligned.
    • State the realised outcome and what you changed for later launches.
  • Read: Site Reliability Engineering (Google) · Design Docs at Google (Industrial Empathy)

Applied and customer scenarios

• A customer wants to automate claims processing with AI. What would you do in the first two weeks?
  • Applied scenario · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you turn a broad request into a bounded, measurable workflow.
  • A strong answer covers:
    • Map the current process, decisions, exceptions and accountable users.
    • Check data access, error consequences and which steps need human judgement.
    • Choose a narrow pilot with a baseline, acceptance criteria and an owner.
  • Read: Building Effective AI Agents (Anthropic) · AI Risk Management Framework (NIST)

Behavioral and values

• Describe a mistake you made, its consequences and what you changed afterward.
  • Behavioral · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you take responsibility and show learning through concrete behaviour.
  • A strong answer covers:
    • Describe your decision and what you knew at the time.
    • Explain impact, recovery and your responsibility without blaming others.
    • Identify a lasting change and evidence that it helped.
  • Read: Postmortem Culture: Learning from Failure (Google)
• Which project are you most proud of, and why?
  • Self-presentation · Senior · Asked at: OpenAI †
  • Tests: Whether you explain meaningful impact and your actual contribution.
  • A strong answer covers:
    • Choose the project by what changed for its users or the business, not by its technology or brand.
    • Separate what you personally decided and built from the team's work, and name the hardest call you made.
    • Close with evidence of the impact and why this project, rather than a bigger one, matters to you.
  • Read: Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research)
• Describe a conflict with a colleague, how you handled it and what you learned.
  • Behavioral · Senior · Asked at: OpenAI †
  • Tests: Whether you understand the other person's perspective and your own part in the conflict.
  • A strong answer covers:
    • Pick a conflict in which your own behaviour contributed, not one where the colleague was simply difficult.
    • Restate the colleague's view as they would, including the pressure they were under, before your own.
    • Close with how the working relationship stood afterwards and what you now do differently yourself.
  • Read: Communication (GitLab Handbook)
• Tell me about conflicting stakeholder priorities and how you established alignment.
  • Behavioral · Senior · Asked at: OpenAI † · ✍ Answer
  • Tests: Whether you make competing objectives explicit and build an actionable agreement.
  • A strong answer covers:
    • Name the stakeholders and the objective each was actually measured on.
    • Show how you surfaced the conflict as a trade-off rather than a personality clash.
    • End with the agreement, what was given up, and evidence that it held.
  • Read: Organizational Change Management (GitLab Handbook)
• How do you manage conflict when the situation is urgent?
  • Applied scenario · Senior · Asked at: OpenAI †
  • Tests: Whether urgency changes the decision process without eliminating respect and accountability.
  • A strong answer covers:
    • Make the decision owner and deadline explicit first, so urgency shortens debate rather than removing it.
    • Hear each side briefly, prefer a reversible step, and record dissent instead of overriding it silently.
    • Revisit after the pressure passes: review the decision, repair relationships, and fix what caused the urgency.
  • Read: Site Reliability Engineering (Google)
• What is the largest failure you have experienced as a product manager?
  • Behavioral · Senior · Asked at: OpenAI †
  • Tests: Whether you can discuss product judgement, consequences and changed practice honestly.
  • A strong answer covers:
    • Pick a failure that is truly large and rooted in your own product judgement, not in others' execution.
    • Reconstruct what you believed then, which signal you discounted, and what it cost users and the business.
    • Close with the practice you changed, such as how you validate demand, and a later decision it improved.
  • Read: Postmortem Culture: Learning from Failure (Google)

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