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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
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- 546
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- 11
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- 8
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Showing 3001–3030 of 4616 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 - 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 - 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 - 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 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 EngineerFollow One-Way Links to the Endpoint Node or the Last Node Before a Loop Closes
Given one-way links in which every node has at most one outgoing link, follow the links from a start node and return the endpoint where the walk stops, or the last node visited before the walk would revisit a node. Tests single-successor graph traversal, visited-node bookkeeping and loop edge cases.
Coding & AlgorithmsTechnical Screen - OpenAIMediumSoftware EngineerGenerate Data Labeling Schedules
This question evaluates combinatorial reasoning and constructive algorithm design for generating constrained assignment schedules, including ensuring each human appears a minimum number of times, preventing duplicate human-task pairs, and managing valid ID ranges.
Coding & AlgorithmsTechnical Screen - OpenAIHardData ScientistHandle repeated churn in SQL
This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsTime Series - OpenAIHardSoftware EngineerHiring manager call: walk through your past work, interests and latest project
A hiring manager asks you to describe what you have built, what you want to work on next, and the project you worked on most recently, then describes their own team. Tests whether you can tell a concise career story, separate your own contribution from the team's, and defend the technical decisions of a recent project in depth.
Behavioral & LeadershipTechnical Screen - OpenAIMediumData ScientistHow would you evaluate a free-trial A/B test?
Evaluates skills in A/B test design and causal inference within the Analytics & Experimentation domain, emphasizing precise metric definition (including guardrails), choice of analysis population (ITT vs per‑protocol), handling of post‑treatment selection, and interpretation of binary conversion and retention metrics; the abstraction level is technical and oriented toward intermediate-to-senior data scientists who must combine statistical rigor with product impact thinking. Commonly asked because it tests the ability to balance acquisition versus downstream retention and revenue, to identify typical analysis pitfalls (time-window errors, incorrect joins/denominators, leakage, peeking, and selection bias), and to translate experiment results into a business recommendation linked to value metrics like LTV and payback period.
Analytics & ExperimentationTechnical Screen - OpenAIMediumMachine Learning EngineerImplement 1NN with NumPy
Tests your ability to implement a fast, vectorized 1-Nearest Neighbor (1NN) classifier using NumPy, focusing on memory-efficient distance computations and tie-breaking.
Machine LearningTechnical Screennumpyvectorizationmachine-learning - OpenAIMediumSoftware EngineerImplement A Contiguous Memory Allocator With Free-Block Coalescing
Design a fixed-size contiguous memory allocator supporting allocation and deallocation with immediate adjacent free-block coalescing.
Coding & AlgorithmsTechnical ScreenData StructuresIntervalsBinary Search - OpenAIMediumiOS EngineerImplement A Mobile Chat Interface In An Existing Codebase
Tests your ability to design and implement a streaming chat interface state manager in an existing mobile codebase, focusing on async token streaming, state modeling, cancellation, and error handling.
Software Engineering FundamentalsTechnical ScreeniOSSwiftConcurrency - OpenAIEasySoftware EngineerImplement Chat Event Counter
This question evaluates data structures and algorithms competency, focusing on designing efficient in-memory counters and time-windowed event aggregation for (userId, chatId) pairs; it is categorized under Coding & Algorithms and has a practical implementation focus.
Coding & AlgorithmsTechnical Screen - OpenAIMediumSoftware EngineerImplement GPU credit ledger
This question evaluates a candidate's ability to design efficient stateful systems and algorithmic data structures for tracking per-tenant resource balances, including handling idempotency, negative balance policies, concurrency, and persistence.
Coding & AlgorithmsTechnical Screen - OpenAIMediumSoftware EngineerImplement IPv4 and CIDR iterators
This problem tests your ability to manipulate IP address bit-representations and implement a custom iterator to traverse all addresses within a given CIDR block.
Coding & AlgorithmsTechnical ScreenBitwiseNetworkingIterator - OpenAIMediumSoftware EngineerImplement KV store and plan type conversions
This question evaluates proficiency in designing scalable, time-versioned key-value data structures and in graph-based shortest-path planning for costed type conversions, assessing algorithmic thinking, complexity analysis, and robustness under large-scale constraints in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - OpenAIMediumSoftware EngineerImplement KV store serialization
This question evaluates skills in binary serialization and deserialization, data encoding and layout, type tagging, endianness, checksumming, versioning for forward/backward compatibility, and algorithmic efficiency and space usage for an in-memory key-value store; it falls under the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - OpenAIMediumMachine Learning EngineerImplement Masked Cross-Entropy with Label Smoothing
Implement a numerically stable, masked cross-entropy loss function with label smoothing and temperature scaling in NumPy, returning both the loss and analytical gradients.
Machine LearningTechnical Screenmachine-learningnumpydeep-learning - OpenAIMediumSoftware EngineerImplement Social Follow Recommendations
This problem tests your ability to navigate a social graph to implement a recommendation system using set operations and frequency counting.
Coding & AlgorithmsTechnical ScreenGraphHash MapSorting - OpenAIMediumSoftware EngineerImplement Social Graph Snapshot Queries
This problem tests your ability to design a query-optimized representation for a directed graph, specifically focusing on efficient retrieval of multi-hop relationships and neighbor-based ranking.
Coding & AlgorithmsTechnical ScreenGraphsAdjacency ListData Structures - OpenAIMediumSoftware EngineerImplement Time-Aware GPU Credit Ledger
Design an in-memory credit ledger that tracks GPU compute grants with expiration timestamps, consumes credits greedily by earliest expiration, and supports atomic deductions.
Coding & AlgorithmsTechnical ScreenHeap / Priority QueueDesignGreedy - OpenAIHardMachine Learning EngineerImplement a 1-Nearest-Neighbor Classifier with a Manhattan-Distance Follow-Up
Implement a 1-nearest-neighbor classifier that labels each query point with the label of its closest training point under Euclidean distance, then extend it to Manhattan distance. Tests vectorized distance computation under memory limits, tie handling and how the choice of metric changes predictions.
Machine LearningTechnical Screen
OpenAI