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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.
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Showing 1801–1830 of 3640 results
- LinkedInMediumSoftware EngineerTraverse a Binary Tree in Zigzag Level Order
Return binary-tree values level by level while alternating left-to-right and right-to-left order without changing level membership.
Coding & AlgorithmsOnsite - Lucid MotorsMediumProduct ManagerFavorite Product Deep Dive & UX Focus
Practice a favorite product and UX improvement interview case with a past UX-focused product story, jobs-to-be-done analysis, product positioning, key metrics, concrete improvement ideas, experiments, guardrails, and prioritization trade-offs.
Product / Decision MakingOnsite - Lucid MotorsMediumProduct ManagerLucid Fit & Career Motivation
Practice Lucid Motors Product Manager fit interviews with resume storytelling, company knowledge, motivation, next-role criteria, and UX experience. The solution helps connect product achievements to premium electric vehicle software, ownership journeys, and customer experience.
Behavioral & LeadershipOnsite - Luma AIMediumSoftware EngineerBuild Collaborative Document Editing Shared by People and AI Agents
A take-home asks you to build collaborative document editing in which people and AI agents edit the same document, then defend its scaling limits and add approval, audit history and selective revert for agent edits. Tests concurrency control, real-time sync, anchoring slow agent edits, attribution, and prototype scoping.
System DesignOnsite - Luma AIMediumSoftware EngineerBuild a Scalable Text-to-Speech Service Prototype, Then Defend and Extend It
An 8 to 12 hour take-home asks you to build a scalable text-to-speech service with AI coding agents, then defend its trade-offs and scaling limits in a product review and plan a new customer requirement. Tests 0-to-1 scoping, streaming and batch synthesis design, capacity reasoning, and estimation and roadmap skills.
System DesignOnsite - Luma AIMediumSoftware EngineerExplain Your Motivation and How You Use AI Development Tools in Your Workflow
A recruiter screen for an AI product engineering role asks about your motivation for moving, why this company, your hands-on experience with AI development tools and workflows, and which areas of development interest you. Tests concrete, verifiable examples of agent-assisted work and a coherent career story.
Behavioral & LeadershipOnsite - Luma AIMediumSoftware EngineerReverse-Engineer an App's Undocumented API and Rebuild It With Better UX
A take-home asks you to reverse-engineer the undocumented API behind an existing app and rebuild the app with a better user experience, then review its fragility and extend it with safe write support. Tests API discovery, schema inference, resilient client design, credential and legal judgment, and UX scoping.
Software Engineering FundamentalsOnsite - LyftMediumData ScientistAssess Cultural Fit Through Behavioral Interview Questions
Assess Cultural Fit Through Behavioral Interview Questions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - LyftMediumSoftware EngineerAssign Tasks to the Minimum Number of Workers
Assign interval tasks to the minimum number of workers while preserving deterministic worker IDs and original output order. Reason about overlapping times, tie handling, resource reuse, efficient scheduling at scale, and boundary cases where one task ends as another begins.
Coding & AlgorithmsOnsite - LyftMediumData ScientistCompute Poisson supply–demand match probability
This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.
Statistics & MathOnsite - LyftMediumData ScientistDemonstrate leadership under ambiguity
This question evaluates leadership, stakeholder communication, ambiguity management, and negotiation competencies for a Data Scientist role, covering problem scoping, assumption documentation, rapid validation, cross-cultural interactions, and data-backed compensation discussions.
Behavioral & LeadershipOnsite - LyftMediumSoftware EngineerDescribe a challenging resume project
Describe a challenging resume project evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - LyftMediumSoftware EngineerDescribe a failure and learning
This question evaluates a candidate's leadership, accountability, stakeholder communication, reflection, and ability to learn from failure within software engineering projects.
Behavioral & LeadershipOnsite - LyftMediumSoftware EngineerDesign One-to-One Messaging Across Multiple Devices
Tests the ability to architect a real-time, one-to-one messaging system supporting multi-device synchronization, push notifications, idempotent message delivery, and reliable cursor-based history pagination.
System DesignOnsitesystem-designdistributed-systemsmessaging - LyftMediumSoftware EngineerDesign a Distributed Web Crawler
Design a distributed web crawler that starts from seed URLs and scales across roughly 1,000 heterogeneous worker devices. The question covers canonical URL identity and crawl scope, a durable partitioned frontier with atomic deduplication, host-level politeness and robots handling, lease-based coordination with at-least-once delivery and idempotent effects, fetch/parse/storage schemas, near-duplicate detection, recrawl and backpressure, capacity estimation, safety controls, and the enqueue/status/results API. A common Lyft software engineer onsite system design question testing distributed-systems judgment, correctness under retries, and operational thinking.
System DesignOnsite - LyftHardSoftware EngineerDesign a Donation Platform
A Lyft onsite system design question: design an online donation platform that processes one-time and recurring donations through an external payment provider while guaranteeing no lost, duplicated, or wrong-amount charges. It tests payment-flow correctness, idempotency, append-only ledgers, reconciliation, data modeling, consistency tradeoffs, and scaling for donation spikes.
System DesignOnsite - LyftEasySoftware EngineerDesign a Fault-Tolerant Wikipedia Archiver
Design a fault-tolerant system that archives Wikipedia and keeps versioned content current using one reliable coordinator, many unreliable untrusted workers, and durable blob storage. Cover policy-aware discovery, large-frontier deduplication, task assignment, result validation, safe publication, failure recovery, and recrawling.
System DesignOnsite - LyftMediumSoftware EngineerDesign a Globally Versioned Key-Value Store
Design a globally versioned key-value store with sparse key histories, binary-search reads, atomic version publication, and correct history retention.
Software Engineering FundamentalsOnsite - LyftMediumSoftware EngineerDesign a One-to-One Chat Service
Design a durable one-to-one chat service with conversation ordering, idempotent sends, offline replay, and secure multi-device delivery.
System DesignOnsite - LyftMediumSoftware EngineerDesign a Transactional In-Memory Key-Value Store
Design a transactional in-memory key-value store with correct value counts, efficient rollback, and explicitly defined command semantics.
Software Engineering FundamentalsOnsite - LyftMediumSoftware EngineerDesign a Wiki Crawler That Keeps Every Stored Page at Most Five Days Stale
A system design question about crawling and storing all the content of a wiki while keeping every stored page no more than five days stale. It tests capacity estimates, the crawl frontier and scheduling, change detection, storage of page versions, politeness limits, failure recovery, and monitoring of freshness.
System DesignOnsite - LyftHardSoftware EngineerDesign a scalable real-time chat system
This question evaluates skills in designing scalable real-time distributed systems, covering API design, message ordering and idempotency, fan-out strategies, storage tiers, indexing, sharding and replication, consistency vs.
System DesignOnsite - LyftMediumSoftware EngineerDesign a temporal key-value store with historical reads
This question evaluates a candidate's grasp of data structures and algorithms needed to implement a temporal key-value store supporting timestamped set/get operations and efficient historical reads, including performance targets like O(log n) per operation.
Coding & AlgorithmsOnsite - LyftMediumData ScientistDesign experiments for marketplace balance
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, statistical power calculation, and operational rollout planning for two-sided marketplaces, and it is commonly asked to assess the ability to account for interference, supply constraints, spillovers, and contamination when measuring treatment effects. Categorized under Analytics & Experimentation, it tests both conceptual understanding and practical application by probing exposure and eligibility definitions, metric and guardrail specification, sample-size and clustering adjustments, recognition of heterogeneous treatment effects and sequential monitoring concerns, variance-reduction considerations, and rollout and fallback decision-making.
Analytics & ExperimentationOnsite - LyftHardSoftware EngineerDesign web crawler for 1000 devices
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design web crawler for 1000 devices states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - LyftMediumSoftware EngineerDiscuss eligibility and behavioral scenarios
This question evaluates behavioral and leadership competencies—including communication, conflict resolution, feedback reception, end-to-end ownership, prioritization, and employment logistics—within the Behavioral & Leadership domain for a Software Engineer role.
Behavioral & LeadershipOnsite - LyftMediumSoftware EngineerExplain Project Ownership and Collaboration with Colleagues
Tests an engineering candidate's ability to demonstrate end-to-end technical ownership, cross-functional influence, and practical collaboration on a high-impact project.
Behavioral & LeadershipOnsiteBehavioralLeadershipOwnership - LyftHardSoftware EngineerImplement Grid Spread and Transactional Store
This question evaluates skills in grid-based propagation modeling and stateful transactional system design, testing concepts such as graph traversal and timed spread dynamics for the grid task and nested transaction semantics, isolation, and state management for the in-memory key-value store.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerImplement Stateful Fetch-N over a Paginated Upstream
Implement a stateful fetchN wrapper over a paginated upstream that returns items in source order across repeated calls. Define buffering, page-token progression, exhaustion, empty pages, invalid input, and retry behavior without losing or duplicating items.
Software Engineering FundamentalsOnsite - LyftEasySoftware EngineerImplement a Key-Value Store with Nested Transactions
Implement an in-memory string key-value store with lookup, assignment, deletion, value counts, and deeply nested transactions. Define visibility plus exact begin, rollback, and inner or outer commit behavior without copying the entire database per transaction.
Coding & AlgorithmsOnsite
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Luma AI
Lyft