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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 8701–8730 of 9403 results
- UberMediumSoftware EngineerDesign a Rolling Event Counter
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.
Software Engineering FundamentalsTechnical Screen - UberMediumFrontend EngineerDesign a Scalable Calendar Service
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.
System DesignTechnical ScreenAPIDatabaseFrontend - UberMediumMachine Learning EngineerDesign a feed ranking system
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.
ML System DesignTechnical Screen - UberMediumSoftware EngineerDesign a meeting room reservation API
This problem tests your ability to design an in-memory scheduling system that efficiently manages room availability and handles time-interval overlaps.
Software Engineering FundamentalsOnsiteSystem DesignInterval SchedulingData Structures - UberMediumMachine Learning EngineerDesign a restaurant recommendation system
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.
ML System DesignOnsite - UberHardSoftware EngineerDesign a ride-hailing platform like Uber
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.
System DesignOnsite - UberHardSoftware EngineerDesign a search autocomplete system
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.
System DesignOnsite - UberMediumData ScientistDesign airport dispatch with ETA uncertainty
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.
Coding & AlgorithmsTechnical Screen - UberHardData ScientistDesign an A/B test; choose Z vs T
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.
Statistics & MathTechnical Screen - UberHardData ScientistDesign an ETA experiment under interference
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.
Analytics & ExperimentationTechnical Screen - UberMediumSoftware EngineerDesign an Iterative Path Tree and Clarify File Lookup Semantics
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.
Software Engineering FundamentalsTechnical ScreenTreeDFSDesign - UberHardData ScientistDesign an experiment with marketplace network effects
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.
Analytics & ExperimentationTechnical Screen - UberMediumSoftware EngineerDesign an object-oriented parking lot system
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.
Software Engineering FundamentalsTechnical Screen - UberEasyData ScientistDesign and Test a New Feature
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.
Analytics & ExperimentationOnsite - UberHardData ScientistDesign and power an incentive experiment
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.
Analytics & ExperimentationOnsite - UberMediumSoftware EngineerDesign cart and pricing engine classes
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.
System DesignOnsite - UberMediumSoftware EngineerDesign cart management lifecycle service
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.
System DesignTechnical Screen - UberHardSoftware EngineerDesign driver heat map and discuss consensus
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.
System DesignOnsite - UberMediumSoftware EngineerDesign follow/follower classes
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.
Software Engineering FundamentalsTechnical ScreenOODGraphsData Structures - UberMediumSoftware EngineerDesign mkdir and ls for a Basic File System
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.
Software Engineering FundamentalsOnsitetrietreedesign - UberHardData ScientistDesign promo experiment and explain correlation
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.
Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingExperimentation - UberHardSoftware EngineerDesign real-time driver heatmap system
This question evaluates a candidate's ability to design real-time distributed systems for geospatial aggregation, covering skills in spatial partitioning, high-throughput streaming ingestion, low-latency cell counting, WebSocket-based client updates, and top-K ranking within a fault-tolerant multi-region architecture.
System DesignOnsite - UberHardMachine Learning EngineerDesign real-time grid ETA for drivers
This question evaluates a candidate's competency in real-time ML system design, covering streaming data ingestion, GPS noise handling and denoising, online feature computation, low-latency model serving, and operational monitoring.
ML System DesignOnsite - UberHardSoftware EngineerDesign the Data Flow Behind a Food Delivery App's Restaurant List
A system design interview question asking you to design the data flow behind a food delivery app's restaurant list for a customer's delivery address. It tests geographic indexing of delivery areas, propagating restaurant status and delivery-time changes, read-path latency, caching, and freshness trade-offs.
System DesignOnsite - UberMediumSoftware EngineerDesign top-K frequency structure
This question evaluates ability to design efficient in-memory data structures for frequency tracking, including handling recency-based tie-breaking, memory bounds under churn, and concurrency implications.
Coding & AlgorithmsOnsite - UberMediumSoftware EngineerDesign top-K tracker with linked lists
Design top-K tracker with linked lists evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - UberMediumData ScientistDetermine Sample Size for Promotion Campaign A/B Test
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Sample Size for Promotion Campaign A/B Test states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - UberHardSoftware EngineerDetermine Whether a Word Exists in a Character Grid
Practice a Uber coding interview problem focused on determine whether a word exists in a character grid. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.
Coding & AlgorithmsTechnical Screen - UberMediumSoftware EngineerDetermine balanced k values in a permutation
This question evaluates algorithmic reasoning about permutations and contiguous subarray properties, testing skills in array manipulation, positional reasoning, combinatorial insight, and complexity-aware algorithm design within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - UberMediumSoftware EngineerDevelop test plan and TDD for word search
This question evaluates a candidate's competency in designing comprehensive test plans and test-driven development workflows for grid-based word search algorithms, covering edge-case identification, input normalization, path validity, and performance/stress considerations within software testing and algorithmic reasoning.
Software Engineering FundamentalsTechnical Screen
Uber