Uber interview questions

Showing 61–90 of 217 results

• Design a Driver Leaderboard Based on ReviewsMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design a Food Delivery CartMedium

  This question tests the ability to design a distributed, low-latency, and fault-tolerant shopping cart service for a high-concurrency food delivery platform.

  UberSoftware EngineerSystem DesignOnsite
• Design a Food-Delivery Backend (Uber Eats-style)Medium

  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.

  UberSoftware EngineerSystem DesignTechnical Screen
• Design a Highly Available URL ShortenerMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design a Parking Garage Object ModelMedium

  This question evaluates object-oriented design, domain modeling, data structure selection, and concurrency control for resource allocation in a multi-level parking garage.

  UberSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design a Parking LotMedium

  This problem tests object-oriented design principles and the ability to manage resource allocation and concurrency in a multi-threaded environment.

  UberSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design a Real-Time Chat SystemMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design a Real-Time Nearby-Driver ServiceMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design a Ride-Hailing ETA SystemMedium

  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.

  UberData ScientistMachine LearningTechnical Screen
• Design a Rolling Event CounterMedium

  This question evaluates understanding of data structures and time-windowed counting, performance and memory trade-offs, expiration policies, and concurrency when implementing an in-memory rolling event counter.

  UberSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design a Scalable Calendar ServiceMedium

  This problem tests your ability to design a distributed calendar system, focusing on data modeling for recurring events, time zone handling, and frontend state management for a high-concurrency scheduling UI.

  UberFrontend EngineerSystem DesignTechnical Screen
• Design a feed ranking systemMedium

  This question evaluates a candidate's competency in designing end-to-end machine-learning-based feed ranking systems, including setting ranking objectives, candidate generation and filtering, feature design, model training and serving, and operational concerns like freshness, diversity, and scalability.

  UberMachine Learning EngineerML System DesignTechnical Screen
• Design a meeting room reservation APIMedium

  This problem tests your ability to design an in-memory scheduling system that efficiently manages room availability and handles time-interval overlaps.

  UberSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design a restaurant recommendation systemMedium

  An Uber MLE onsite ML system design question: design a personalized restaurant recommendation system for a food delivery marketplace's home feed. It tests candidate generation and ranking, feature engineering and training data, cold-start and exploration, low-latency serving, and offline/online evaluation, plus marketplace concerns like availability, ETA, diversity, fairness, and business rules.

  UberMachine Learning EngineerML System DesignOnsite
• Design a ride-hailing platform like UberHard

  This question evaluates a candidate's competency in large-scale system design, covering distributed systems architecture, data modeling, real-time location streaming, matching algorithms, scalability, fault tolerance, and secure payment handling.

  UberSoftware EngineerSystem DesignOnsite
• Design a search autocomplete systemHard

  This system design question evaluates the ability to architect a large-scale search autocomplete service, testing knowledge of prefix data structures, distributed indexing, and latency-sensitive serving pipelines. It is commonly asked in senior engineering interviews to assess trade-off reasoning between freshness, read performance, and system complexity at scale.

  UberSoftware EngineerSystem DesignOnsite
• Design airport dispatch with ETA uncertaintyMedium

  This question evaluates proficiency in online algorithm design under uncertainty, stochastic modeling of ETAs, multi-objective optimization for resource allocation, and streaming data handling relevant to dispatch systems.

  UberData ScientistCoding & AlgorithmsTechnical Screen
• Design an A/B test; choose Z vs THard

  This question evaluates a candidate's competency in experimental design and statistical inference, covering sample-size and power calculations, the choice between Z and T tests, sequential testing/alpha-spending strategies, and causal-inference planning for non-randomized rollouts.

  UberData ScientistStatistics & MathTechnical Screen
• Design an ETA experiment under interferenceHard

  This question evaluates a data scientist's competency in experimental design and causal inference for two-sided marketplaces, focusing on interference, partial compliance, metric definition, randomization strategy, instrumentation, estimation, and power calculations within the Analytics & Experimentation domain.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Design an Iterative Path Tree and Clarify File Lookup SemanticsMedium

  Design an in-memory directory tree supporting iterative path insertion and file lookup using an explicit stack. The problem tests handling of duplicate component names, memory-efficient tree representation, and clarifying ambiguous file search contracts.

  UberSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design an experiment with marketplace network effectsHard

  This question evaluates a candidate's competency in causal experiment design for networked two‑sided marketplaces, focusing on interference and spillover management, cluster randomization choices, metric definition, power/sample‑size calculations, bias‑reduction approaches, and diagnostic checks.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Design an object-oriented parking lot systemMedium

  This question evaluates object-oriented design competency, including class modeling, responsibility assignment, relationships (inheritance and composition), and familiarity with design patterns and SOLID principles for representing constraints like spot sizes and vehicle types.

  UberSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design and Test a New FeatureEasy

  This question evaluates a candidate's ability to design a new product feature for a ride-hailing platform and to translate product goals into measurable outcomes, testing competencies in product sense, metric definition, experiment and causal-inference design, segmentation, and two-sided marketplace trade-off analysis.

  UberData ScientistAnalytics & ExperimentationOnsite
• Design and power an incentive experimentHard

  This question evaluates experimental design, causal inference, A/B testing and statistical power calculation skills, including defining eligibility, unit of randomization, treatment arms, stratification, metric selection with guardrails, handling noncompliance and interference, and interpreting heterogeneous treatment effects.

  UberData ScientistAnalytics & ExperimentationOnsite
• Design cart and pricing engine classesMedium

  This question evaluates object-oriented system design and domain modeling skills related to cart operations, item customizations, composable pricing rules (surge, promotions, membership) and producing an itemized receipt.

  UberSoftware EngineerSystem DesignOnsite
• Design cart management lifecycle serviceMedium

  This question evaluates system design and backend engineering competencies, focusing on data modeling, indexing strategy, concurrency control, transactional correctness, and scalability for a multi-device cart lifecycle and one-time checkout.

  UberSoftware EngineerSystem DesignTechnical Screen
• Design driver heat map and discuss consensusHard

  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 driver heat map and discuss consensus states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  UberSoftware EngineerSystem DesignOnsite
• Design follow/follower classesMedium

  This problem tests your ability to design a system for managing directed relationships between users, requiring careful selection of data structures to balance write performance and query latency.

  UberSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design mkdir and ls for a Basic File SystemMedium

  Design an in-memory file system supporting path creation (`mkdir`) and directory listing (`ls`). Tests tree-based data structures, path parsing, and clean object-oriented design.

  UberSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design promo experiment and explain correlationHard

  This problem tests your ability to distinguish correlation from causation in observational data and to design a robust A/B test to measure the incremental impact of a new rider promotion.

  UberData ScientistAnalytics & ExperimentationTechnical Screen