OpenAI interview questions

Showing 181–210 of 342 results

• Design an enterprise RAG assistant for internal docsHard

  This question evaluates expertise in designing and training Retrieval-Augmented Generation (RAG) systems, including retriever, evaluator (reranker/verifier/filter), and generator components, with emphasis on model architecture choices, training objectives, data preparation under privacy and document-permission constraints, and evaluation strategies for grounded answers with citations. It is commonly asked to probe advanced ML system design and operationalization skills for mitigating hallucination, stale or conflicting sources, and long-document retrieval; the category is ML System Design and the level is practical application-focused with detailed modeling and training considerations.

  OpenAISoftware EngineerML System DesignTechnical Screen
• Design an image/video near-duplicate detection systemHard

  This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.

  OpenAIMachine Learning EngineerML System DesignOnsite
• Design an in-memory databaseHard

  This problem tests the ability to architect a high-performance, in-memory key-value store, focusing on concurrency control, data structures, and durability guarantees.

  OpenAISoftware EngineerSystem DesignTechnical Screen
• Design an in-memory key-value databaseHard

  Design an in-memory key-value database evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  OpenAISoftware EngineerSystem DesignTechnical Screen
• Design and Defend a Free-Trial Conversion AnalysisHard

  Walk through how to plan, execute, and defend a free-trial conversion analysis end to end: cohort definition, data-quality checks, standards for a credible insight, and a recommendation deck that survives live pushback.

  OpenAIData ScientistAnalytics & ExperimentationOnsite
• Design and optimize a RAG systemHard

  An OpenAI machine learning engineer onsite ML system design question: design, optimize and evaluate a production Retrieval-Augmented Generation (RAG) system that answers questions over a private corpus with citations. The merged answer covers the full pipeline — ingestion and layout-aware parsing, structure-aware chunking, embeddings and versioning, hybrid BM25 plus vector retrieval with RRF fusion, cross-encoder reranking, grounded generation with forced citations, abstention and a fallback ladder, ACL enforcement during retrieval, and the insert/edit/delete freshness lifecycle. It then gives a stage-by-stage evaluation plan (chunk answerability, hit-rate@K, recall@K, nDCG, faithfulness, citation quality, end-to-end success), how to bootstrap labels with no gold set, and follow-ups on debugging a wrongly cited document, embedding-model upgrades, multi-hop versus list-all queries, near-duplicates, conflicting sources and validating an LLM judge.

  OpenAIMachine Learning EngineerML System DesignOnsite
• Design animal translator and premium tierMedium

  Answer product strategy prompts for an animal-human translation product and premium AI subscription tier. The solution covers dog-owner beachhead, collar-plus-app MVP, trust and safety, GTM, pricing, power-user segmentation, Pro packaging, cannibalization, and launch metrics.

  OpenAIProduct ManagerProduct Design & StrategyTechnical Screen
• Design coffee-shop card payments with tips and nightly batch settlement at scaleMedium

  Design a card payment system for coffee shops where purchases are authorized at the counter, tips are added afterwards, and transactions settle in a nightly batch. The discussion centers on scaling nightly settlement, along with idempotent APIs, failure handling and reconciliation against processor reports.

  OpenAISoftware EngineerSystem DesignOnsite
• Design credit balance with vector-clock expirationsHard

  This question evaluates understanding of distributed-systems concepts such as vector clocks, partial orders, concurrency control and idempotency, plus practical competencies in data structures and algorithms for efficient per-user state management and correctness under concurrent operations.

  OpenAISoftware EngineerSystem DesignTechnical Screen
• Design enterprise RAG search systemHard

  This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  OpenAIMachine Learning EngineerML System DesignTechnical Screen
• Design in-memory database APIMedium

  This question evaluates a candidate's ability to design and reason about in-memory data storage, indexing, query planning, and the underlying data structures and algorithms for insert and query operations.

  OpenAISoftware EngineerSystem DesignTechnical Screen
• Design power grid device monitoring over an unreliable public internetMedium

  Design a monitoring system for power grid field devices that report their status over an unreliable public internet. It tests store-and-forward reporting, idempotent ingestion of late and duplicate data, telling silent devices from failed ones, alerting, and security for critical infrastructure.

  OpenAISoftware EngineerSystem DesignOnsite
• Design the GPU Job Scheduler for a Text-to-Video Generation ServiceHard

  Design a high-throughput, fault-tolerant GPU job scheduler tailored for large-scale text-to-video generation workloads under sustained compute contention. The problem evaluates distributed resource allocation, multi-node gang scheduling, fairness guarantees, and preemption mechanics.

  OpenAISoftware EngineerSystem DesignTechnical Screen
• Design webhook, POI, chat, CI/CD, paymentsMedium

  This question evaluates proficiency in distributed system design, covering scalability, availability, durability, data modeling, geospatial indexing, event-driven delivery, API design, fault tolerance, security, and operational monitoring for high-throughput backend services.

  OpenAISoftware EngineerSystem DesignOnsite
• Determine Metrics to Measure Free-Trial Impact on SubscriptionsMedium

  Determine Metrics to Measure Free-Trial Impact on Subscriptions evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  OpenAIData ScientistAnalytics & ExperimentationOnline Assessment
• Diagnose Transformer training and inference bugsHard

  This question evaluates practical debugging skills for Transformer-based sequence models, targeting machine learning engineers who must diagnose intermittent shape/dtype errors and convergence failures simultaneously. It tests deep familiarity with the full training stack — data pipelines, attention masking, positional encoding, optimizer precision, and distributed setup — and the ability to reason about how a single root cause can manifest across multiple symptoms.

  OpenAIMachine Learning EngineerMachine LearningTechnical Screen
• Dispatch Parcels to Delivery Drones That May Go Silent Mid-FlightMedium

  Build an in-process dispatcher that hands delivery parcels to drones, reclaims a parcel when its drone goes silent, rejects stale reports from a drone that already lost its parcel, and quarantines parcels that fail too many times.

  OpenAISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Durable Session Cache: Byte Checkpoints, Segments, Thread SafetyHard

  A three-part exercise building a durable in-memory session cache: serialize entries to length-prefixed bytes and checkpoint them to disk, split the checkpoint into size-capped segments that reassemble by concatenation, then discuss making the class thread-safe. Tests byte-level encoding, exact round-tripping, and concurrency reasoning.

  OpenAISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Evaluate a free-trial A/B testEasy

  This question tests your ability to design an A/B test for a freemium product, interpret statistical results, and address potential biases like cannibalization or selection effects.

  OpenAIData ScientistAnalytics & ExperimentationTechnical Screen
• Expiring GPU Credit Ledger with Out-of-Order Events and Point-in-Time BalanceMedium

  A two-part coding exercise to build a GPU credit ledger with expiring grants, usage deducted earliest-expiration-first, out-of-order events and balance queries at any timestamp, then avoid replaying from time zero on every query. It tests event ordering, heap use, checkpointing and invalidation when late events arrive.

  OpenAISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Explain JS threading and useState vs useRefHard

  This question evaluates understanding of JavaScript's execution model and concurrency mechanisms (including worker-based threading) along with React state-management semantics, specifically the behavioral differences between useState and useRef.

  OpenAISoftware EngineerSoftware Engineering FundamentalsHR Screen
• Explain KV cache in Transformer inferenceMedium

  This question evaluates understanding of KV cache mechanisms in Transformer inference, including attention-state caching, memory and latency trade-offs, and engineering optimizations for autoregressive decoding.

  OpenAIMachine Learning EngineerSoftware Engineering FundamentalsOnsite
• Explain Why You Want This Company and Infrastructure RoleEasy

  This question assesses the candidate's ability to connect their personal career trajectory with the specific engineering challenges of scaling large-scale AI infrastructure, testing alignment, motivation, and self-awareness.

  OpenAISoftware EngineerBehavioral & LeadershipHR Screen
• Explain Your Engineering OwnershipHard

  This question evaluates engineering ownership, technical leadership, end-to-end system responsibility, trade-off reasoning, and incident diagnosis by asking about recent hands-on work, technology choices, an owned system, and a production failure and fix.

  OpenAISoftware EngineerBehavioral & LeadershipHR Screen
• Explain Your Technical Focus and Responsible Use of AIMedium

  Assesses a candidate's technical ownership, practical experience integrating AI systems or tools, and their ability to navigate reliability, safety, and ethical trade-offs.

  OpenAISoftware EngineerBehavioral & LeadershipHR Screen
• Explain motivation and mission alignmentHard

  This question evaluates motivation, mission alignment, cultural fit, and communication skills by probing personal values, long-term goals, and the ability to articulate congruence with an organization's purpose.

  OpenAIMachine Learning EngineerBehavioral & LeadershipOnsite
• Explain what torch.distributed.barrier doesHard

  This question evaluates understanding of PyTorch distributed synchronization primitives, process coordination, and concurrency control in distributed training workflows.

  OpenAIMachine Learning EngineerSoftware Engineering FundamentalsOnsite
• Explain your perspective on AI safetyHard

  An OpenAI software engineer onsite behavioral question asking you to define AI safety in practical product terms, triage near-term versus long-term risks, and assess AI's effect on human work, the economy, and society. The answer must then connect those views to concrete engineering practice — threat modeling, guardrails, evals and red-teaming, phased rollout, and incident response. It also tests whether you can balance responsible deployment against shipping speed in both directions.

  OpenAISoftware EngineerBehavioral & LeadershipOnsite
• Extensible card-hand ranking: three of a kind, flush, pair and a discard ruleMedium

  A three-part object-oriented coding exercise built around a card game: rank hands by three of a kind, pair and high card, insert a flush category, then add a discard rule that removes hands that are not a pair, returning a log of each game. It tests extensible design, deterministic tie-breaking and absorbing rule changes cleanly.

  OpenAIApplied ScientistSoftware Engineering FundamentalsOnsite
• Fault-Tolerant Task Queue with Retries, a Dead-Letter Queue and Task DependenciesHard

  Implement an in-memory fault-tolerant task queue with enqueue, reserve, complete and fail operations, retries up to a configurable attempt limit, and a dead-letter queue for tasks that keep failing. A second part adds dependencies between tasks, testing state-machine design, efficient readiness tracking and handling of failed dependencies.

  OpenAISoftware EngineerSoftware Engineering FundamentalsTechnical Screen