practice a little every day, paws crossed ♡
Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 6301–6330 of 9403 results
- OpenAIMediumProduct ManagerDesign animal translator and premium tier
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.
Product Design & StrategyTechnical Screen - OpenAIMediumSoftware EngineerDesign coffee-shop card payments with tips and nightly batch settlement at scale
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.
System DesignOnsite - OpenAIHardSoftware EngineerDesign credit balance with vector-clock expirations
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.
System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign enterprise RAG search system
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.
ML System DesignTechnical Screen - OpenAIMediumSoftware EngineerDesign in-memory database API
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.
System DesignTechnical Screen - OpenAIMediumSoftware EngineerDesign power grid device monitoring over an unreliable public internet
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.
System DesignOnsite - OpenAIHardSoftware EngineerDesign the GPU Job Scheduler for a Text-to-Video Generation Service
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.
System DesignTechnical ScreenGPU SchedulingDistributed SystemsResource Management - OpenAIMediumSoftware EngineerDesign webhook, POI, chat, CI/CD, payments
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.
System DesignOnsite - OpenAIMediumData ScientistDetermine Metrics to Measure Free-Trial Impact on Subscriptions
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.
Analytics & ExperimentationOnline Assessment - OpenAIHardMachine Learning EngineerDiagnose Transformer training and inference bugs
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.
Machine LearningTechnical Screen - OpenAIMediumSoftware EngineerDispatch Parcels to Delivery Drones That May Go Silent Mid-Flight
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.
Software Engineering FundamentalsTechnical Screenconcurrencystate-machinereliability - OpenAIHardSoftware EngineerDurable Session Cache: Byte Checkpoints, Segments, Thread Safety
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.
Software Engineering FundamentalsTechnical Screenserializationpersistenceconcurrency - OpenAIEasyData ScientistEvaluate a free-trial A/B test
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.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsRetention - OpenAIMediumSoftware EngineerExpiring GPU Credit Ledger with Out-of-Order Events and Point-in-Time Balance
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.
Software Engineering FundamentalsTechnical Screen - OpenAIHardSoftware EngineerExplain JS threading and useState vs useRef
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.
Software Engineering FundamentalsHR Screen - OpenAIMediumMachine Learning EngineerExplain KV cache in Transformer inference
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.
Software Engineering FundamentalsOnsite - OpenAIEasySoftware EngineerExplain Why You Want This Company and Infrastructure Role
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.
Behavioral & LeadershipHR ScreenInfrastructureBehavioralCareer Strategy - OpenAIHardSoftware EngineerExplain Your Engineering Ownership
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.
Behavioral & LeadershipHR Screen - OpenAIMediumSoftware EngineerExplain Your Technical Focus and Responsible Use of AI
Assesses a candidate's technical ownership, practical experience integrating AI systems or tools, and their ability to navigate reliability, safety, and ethical trade-offs.
Behavioral & LeadershipHR ScreenBehavioralAI SafetyTechnical Ownership - OpenAIHardMachine Learning EngineerExplain motivation and mission alignment
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.
Behavioral & LeadershipOnsite - OpenAIHardMachine Learning EngineerExplain what torch.distributed.barrier does
This question evaluates understanding of PyTorch distributed synchronization primitives, process coordination, and concurrency control in distributed training workflows.
Software Engineering FundamentalsOnsite - OpenAIHardSoftware EngineerExplain your perspective on AI safety
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.
Behavioral & LeadershipOnsite - OpenAIMediumApplied ScientistExtensible card-hand ranking: three of a kind, flush, pair and a discard rule
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.
Software Engineering FundamentalsOnsite - OpenAIHardSoftware EngineerFault-Tolerant Task Queue with Retries, a Dead-Letter Queue and Task Dependencies
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.
Software Engineering FundamentalsTechnical Screen - OpenAIHardSoftware EngineerFault-Tolerant Task Queue with Retry Cooldowns, a Dead-Letter Queue and Dependencies
Implement an in-memory fault-tolerant task queue with enqueue, reserve, fail and a dead-letter queue, where each failed task waits out its own cooldown measured from the failure time and returns in its original enqueue order. A second part adds task dependencies, testing state modeling, efficient scheduling and careful edge-case handling.
Software Engineering FundamentalsTechnical Screen - OpenAIHardSoftware EngineerFault-Tolerant Work Queue with Leases, Fencing Tokens and DAG Task Dependencies
Implement a fault-tolerant work queue in which workers claim tasks under expiring leases, crashed workers' tasks are reassigned, and late reports from stale workers are rejected, then add DAG dependencies with cycle detection. It tests leases, fencing tokens, topological release and delivery semantics.
Software Engineering FundamentalsOnsite - OpenAIMediumMachine Learning EngineerFilter Bad Human Annotations
Tests the ability to detect and filter low-quality human labels in large-scale dataset pipelines by modeling annotator reliability, distinguishing genuine ambiguity from noise, and evaluating downstream model performance.
Machine LearningOnsitedata-centric-airlhflatent-variable-models - OpenAIMediumMachine Learning EngineerFind Minimum Compatible Version
Implement an algorithm to find the earliest compatible software version while minimizing expensive compatibility-check API calls across partitioned version branches.
Coding & AlgorithmsOnsiteBinary SearchAlgorithmsOptimization - OpenAIHardSoftware EngineerFind a Hidden Number When Each Comparison Answer Arrives One Call Late
Find a hidden integer from 1 to n using a comparison API whose answers arrive one call late: each call submits a new guess but returns the result for the previous one. Build a correct search first, then reduce the worst-case number of calls. It tests binary search under delayed feedback and worst-case analysis.
Software Engineering FundamentalsOnsite - OpenAIMediumMachine Learning EngineerFind the First Working Version
This question tests the ability to combine custom comparison logic with a binary search approach to identify the first transition point in a monotonic sequence.
Coding & AlgorithmsOnsiteBinary SearchAlgorithmsOptimization
OpenAI