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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
- Questions
- 546
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- 11
- Topics
- 8
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Showing 8671–8700 of 9403 results
- UberMediumSoftware EngineerCount subarrays with ≥k identical pairs
This question evaluates proficiency in array algorithms, frequency-based counting and combinatorial reasoning for identifying equal-element pairs within contiguous subarrays.
Coding & AlgorithmsOnline Assessment - UberMediumSoftware EngineerDeep Equality of Two Records
This problem tests your ability to recursively compare two complex, nested data structures while handling different types like dictionaries, lists, and primitives.
Coding & AlgorithmsTechnical ScreenRecursionData StructuresHash Maps - UberMediumSoftware EngineerDefine and integrate room ranking factors
Define and integrate room ranking factors 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 & ExperimentationOnsite - UberMediumData ScientistDerive a CDF from a PDF
This question tests the ability to derive a Cumulative Distribution Function (CDF) from a given Probability Density Function (PDF), ensuring proper integration and boundary conditions.
Statistics & MathTechnical ScreenProbabilityCalculusStatistics - UberMediumData ScientistDescribe Conflict and Impact
This question set evaluates leadership, communication, stakeholder management, decision-making under ambiguity, and technical project ownership for a data scientist, including the ability to quantify impact and reflect on failures.
Behavioral & LeadershipOnsite - UberMediumSoftware EngineerDescribe a Trade-off Design Change
This question evaluates system design trade-off decision-making, technical leadership, and communication by probing how an individual balances competing goals and constraints when changing an architecture.
Behavioral & LeadershipOnsite - UberMediumMachine Learning EngineerDescribe an ML system you built
This question evaluates end-to-end machine learning system design and ownership — covering problem framing, data and feature engineering, model choice, training and evaluation, deployment, monitoring, and measured impact — and it probes interpersonal leadership in handling conflicts over model decisions, metrics, or product trade-offs.
Behavioral & LeadershipTechnical Screen - UberMediumData ScientistDescribe ownership and failure
This question evaluates ownership, judgment, self-awareness, and the ability to communicate technical depth to non-experts and senior stakeholders, while also probing data-science-specific competencies such as forecasting, experimentation, causal inference, and measuring impact.
Behavioral & LeadershipOnsite - UberMediumSoftware EngineerDescribe your strongest project
This question evaluates a candidate's technical leadership, ownership, communication, and ability to describe design trade-offs, metrics, and problem-solving within a software engineering project.
Behavioral & LeadershipOnline Assessment - UberMediumSoftware EngineerDesign A URL Shortener
This question tests your ability to design a high-scale, distributed URL shortening service that handles billions of requests while ensuring low latency and data durability.
System DesignOnsiteDistributed SystemsCachingScalability - UberHardSoftware EngineerDesign Distributed Storage for Large Structured Data
Design distributed storage with versioned metadata, block replicas, layered caches, failure repair, locality-aware jobs, and resource-aware load balancing.
System DesignOnsite - UberHardData ScientistDesign ETA prediction for Uber rides
This question evaluates competency in end-to-end machine learning system design for real-time spatiotemporal ETA prediction, encompassing feature engineering from heterogeneous data sources, uncertainty quantification, model selection, and production serving and monitoring.
Machine LearningTechnical Screen - UberHardSoftware EngineerDesign Food Delivery Cart
This question tests the ability to design a distributed, highly concurrent shopping cart service for a food delivery platform, focusing on transactional integrity, race conditions, and eventual consistency.
System DesignOnsiteConcurrencyDistributed SystemsCaching - UberMediumSoftware EngineerDesign MapReduce for schedule aggregation
Design MapReduce for schedule aggregation 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.
System DesignOnsite - UberHardSoftware EngineerDesign MapReduce for schedules
This question evaluates proficiency in scalable stream and batch data processing, windowed aggregation, event-time semantics, deduplication, skew mitigation, and distributed computation using MapReduce/Spark paradigms; it falls under System Design and Big Data/stream-processing and is commonly asked to assess designing correct, high-throughput windowed aggregations and handling late or duplicated events at scale. The level of abstraction is practical application with architectural and operational considerations rather than low-level coding, and this English summary frames the competency and interview focus for search engines.
System DesignOnsite - UberMediumData ScientistDesign Pricing Model Experiment
This question evaluates experimental design and causal inference competencies for marketplace pricing, covering metric selection, guardrail definition, interference and switchback designs, and sample-size and treatment-window tradeoffs.
Analytics & ExperimentationTechnical Screen - UberMediumSoftware EngineerDesign Product Page View Tracking
This question evaluates a candidate's ability to design scalable, reliable event-collection and analytics systems, covering client- and server-side instrumentation, event schemas/APIs, counting total versus unique views, handling high write volumes for popular products, storage choices for raw events and aggregated counters, and operational concerns such as bot filtering, deduplication, privacy, fraud prevention, freshness, reliability, and monitoring. It is commonly asked in the System Design domain to assess architectural thinking and operational trade-offs, testing both conceptual understanding of distributed systems and practical application of scalable data pipelines, storage, and observability.
System DesignOnsite - UberMediumData ScientistDesign Rideshare Marketplace Causal Analyses
This question evaluates a data scientist's skills in experimental design and causal inference, marketplace metrics and diagnostics, fairness and safety impact assessment, incentive and policy evaluation, and ETA modeling and forecast quality measurement.
Analytics & ExperimentationOnsite - UberMediumSoftware EngineerDesign Stock Price Alerts
This question evaluates a candidate's ability to design scalable, low-latency alerting and notification systems, covering system architecture, APIs, data modeling, event-driven alert evaluation, deduplication, and multi-channel delivery; it is in the System Design category and assesses practical application skills.
System DesignTechnical Screen - UberHardSoftware EngineerDesign Uber Eats-style search function
An Uber Software Engineer system design onsite question: design the search function for an Uber Eats-style food delivery platform, covering APIs, data ingestion, geo-aware indexing and retrieval, multi-stage ranking, real-time availability updates, scaling, caching, fault tolerance, monitoring, and freshness-vs-latency trade-offs. It tests the ability to build a low-latency, location-aware, highly available search system with strong observability.
System DesignOnsite - UberMediumSoftware EngineerDesign a Distributed Logging System
This problem evaluates the ability to design a high-throughput, fault-tolerant logging infrastructure capable of ingesting and storing massive volumes of telemetry data from diverse distributed services.
System DesignOnsiteDistributed SystemsScalabilityData Pipeline - UberMediumSoftware EngineerDesign a Driver Leaderboard Based on Reviews
This problem tests the ability to design a scalable, low-latency leaderboard system that handles high-frequency write throughput from reviews and supports read-heavy leaderboard queries.
System DesignOnsiteScalabilityCachingData Modeling - UberMediumSoftware EngineerDesign a Food Delivery Cart
This question tests the ability to design a distributed, low-latency, and fault-tolerant shopping cart service for a high-concurrency food delivery platform.
System DesignOnsiteDistributed SystemsAPI DesignCaching - UberMediumSoftware EngineerDesign a Food-Delivery Backend (Uber Eats-style)
This problem evaluates your ability to design a distributed system that manages real-time interactions between three distinct user types: customers, restaurants, and couriers, with a focus on geospatial querying and high-concurrency event streaming.
System DesignTechnical ScreenMicroservicesGeospatialCaching - UberMediumSoftware EngineerDesign a Highly Available URL Shortener
This question tests your ability to design a distributed, highly available system that handles high-read traffic and ensures data integrity for short-URL mappings.
System DesignOnsiteScalabilityCachingDatabases - UberMediumSoftware EngineerDesign a Parking Garage Object Model
This question evaluates object-oriented design, domain modeling, data structure selection, and concurrency control for resource allocation in a multi-level parking garage.
Software Engineering FundamentalsOnsite - UberMediumSoftware EngineerDesign a Parking Lot
This problem tests object-oriented design principles and the ability to manage resource allocation and concurrency in a multi-threaded environment.
Software Engineering FundamentalsOnsiteObject-Oriented DesignConcurrencySystem Design - UberMediumSoftware EngineerDesign a Real-Time Chat System
This question tests the ability to design a highly available and scalable real-time chat service, focusing on persistent connections, message routing, and reliable delivery mechanisms.
System DesignOnsiteWebSocketsDistributed SystemsScalability - UberMediumSoftware EngineerDesign a Real-Time Nearby-Driver Service
Tests the ability to architect a high-throughput, low-latency geospatial ingestion and query service that tracks millions of moving entities, handles state transitions, and avoids boundary/hot-spot bottlenecks.
System DesignOnsiteGeospatialDistributed SystemsIn-Memory Caching - UberMediumData ScientistDesign a Ride-Hailing ETA System
This question evaluates a data scientist's competency in end-to-end machine learning system design for spatiotemporal prediction, covering temporal and geospatial feature engineering, model family selection, metric tradeoffs and calibration, selection bias and noise handling, latency and deployment constraints, monitoring, and experiment design.
Machine LearningTechnical Screen
Uber