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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
- Languages
Showing 1141–1170 of 3640 results
- DoorDashHardSoftware EngineerDesign a Food Review System for a Delivery App, Focused on the Database Schema
System design question for the review feature of a food-delivery platform, where customers rate and review the food from delivered orders and others browse ratings. The focus is database schema design: tables, keys, constraints, indexes, rating aggregates, and how edits, deletes and moderation keep them correct.
System DesignOnsite - DoorDashMediumSoftware EngineerDesign a Food Review and Voting System
Design a scalable review and voting platform for dishes and restaurants, focusing on transactional vote changes, aggregate counter consistency, hot-item read/write scaling, and stable pagination.
System DesignOnsiteDistributed SystemsDatabasesCaching - DoorDashHardSoftware EngineerDesign a Food-Item Review System
Design an item-level review and rating platform for food delivery that handles high-throughput reads, asynchronous sentiment and moderation pipelines, and near real-time score aggregations.
System DesignOnsiteDistributed SystemsData ModelingCaching - DoorDashHardData ScientistDesign a Homepage Store Recommender
This question assesses the ability to design an end-to-end, low-latency recommendation system for a food delivery marketplace, focusing on candidate retrieval strategies, real-time personalization, and system architecture.
Machine LearningOnsiteRecommender SystemsSystem DesignRanking - DoorDashHardData ScientistDesign a Low-Latency Store Recommender
This question assesses the ability to architect a multi-stage recommendation pipeline that balances low-latency retrieval with high-accuracy ranking in a geo-aware, high-concurrency food delivery context.
Machine LearningOnsiteSystem DesignRecommendation SystemsLatency - DoorDashMediumSoftware EngineerDesign a Menu Item Review and Rating System for a Food-Delivery Platform
A system design question on a menu review feature for a food-delivery platform, where customers rate and review the menu items they ordered. It tests requirement prioritization, detailed review schema and API design, rating aggregation, caching and moderation, and time management that still reaches a deep dive.
System DesignOnsite - DoorDashHardSoftware EngineerDesign a Multi-Service Workflow Engine
Design a resilient, distributed workflow orchestration engine capable of executing multi-step business processes across heterogeneous services with strict guarantees around durability, idempotency, and failure recovery.
System DesignOnsiteDistributed SystemsSaga PatternWorkflow Engine - DoorDashEasySoftware EngineerDesign a Real-Time Monitoring System
This problem tests your ability to design a high-throughput observability pipeline capable of ingesting, storing, and alerting on time-series metrics from a distributed fleet of services.
System DesignOnsiteObservabilityTime-SeriesDistributed Systems - DoorDashMediumSoftware EngineerDesign a Refund Workflow as a Directed Acyclic Graph
Design a resilient, stateful workflow orchestration engine to process complex order refunds as a Directed Acyclic Graph (DAG), ensuring idempotency, crash recovery, and financial consistency.
System DesignOnsitesystem-designdistributed-systemsworkflow-engine - DoorDashMediumSoftware EngineerDesign a Review and Rating System for Food Delivery Menu Items
Design a review and rating system for a food-delivery platform, where customers review the individual menu items they received and every menu shows each item's rating. It tests data modeling, purchase verification, incremental rating aggregation, caching for read-heavy menus, moderation, and handling bursts on popular items.
System DesignOnsite - DoorDashMediumSoftware EngineerDesign a Sliding-Hour Top-Restaurants Service
This problem tests the ability to design a high-throughput, real-time analytics system that maintains a top-k leaderboard over a sliding time window.
System DesignOnsiteStreamingAnalyticsDistributed Systems - DoorDashMediumSoftware EngineerDesign a Store Recommendation System and Explain ML Trade-offs
Design a personalized store recommendation feed for an on-demand delivery platform and evaluate key machine learning modeling trade-offs.
ML System DesignOnsiteRecommendation SystemsMulti-Task LearningRanking - DoorDashEasyData ScientistDesign a Top Dasher experiment with interference
This problem tests your ability to design a controlled experiment in a marketplace with network effects, specifically focusing on how to mitigate spillover interference between treated and control groups.
Analytics & ExperimentationOnsiteExperimentationCausal InferenceMarketplace - DoorDashHardSoftware EngineerDesign a distributed cron job scheduler
Design a distributed cron job scheduler 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 - DoorDashMediumSoftware EngineerDesign a donations service with 3-day rolling totals
This question evaluates a candidate's competency in designing scalable, low-latency streaming data systems, covering event-time windowing, rolling-window aggregation, idempotency for duplicate events, late/out-of-order handling, and auditability.
System DesignOnsite - DoorDashMediumSoftware EngineerDesign a hierarchical path registry
Design a hierarchical path registry 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 - DoorDashMediumMachine Learning EngineerDesign a scalable recommendation serving system
This question evaluates an engineer's ability to design scalable, low-latency, and resilient online recommendation serving infrastructure, emphasizing caching strategies, partitioning/sharding, data storage consistency, and observability.
System DesignOnsite - DoorDashHardSoftware EngineerDesign an Online Course Marketplace Review System Schema
Design the review and rating feature of an online course marketplace where only learners who completed a course may rate it, with the discussion centered on table design, keys, constraints, and the indexes each read path needs.
System DesignOnsitesystem-designdatabase-designschema - DoorDashHardSoftware EngineerDesign an async donation payment platform
This question evaluates a candidate's ability to design scalable, reliable payment systems and asynchronous processing pipelines, covering competencies in distributed systems, idempotency, data modeling, security/PCI compliance, retry and dead-letter strategies, and observability.
System DesignOnsite - DoorDashHardData ScientistDesign an experiment for order batching
This question evaluates competence in experiment design and causal inference for marketplace interventions, covering choice of randomization unit and mitigation of interference/spillovers, stratification and covariate adjustment, power and sample‑size calculations, detection of novelty/steady‑state effects, heterogeneity estimation, and pre-registered guardrail decision rules. It is commonly asked in the Analytics & Experimentation domain because interviewers need to assess both conceptual understanding of causal inference and the practical application of randomized designs and operational measurement to ensure robust, contamination‑aware evaluation of policy changes.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistDesign and Evaluate a Refund Policy for Delayed Orders
Design an automated, data-driven compensation policy for late deliveries on a three-sided marketplace. Formulate delay thresholds, balance customer lifetime value against operational costs, and structure an A/B test with appropriate randomization and guardrail metrics.
Analytics & ExperimentationOnsiteexperimentationab-testingmarketplace-analytics - DoorDashHardData ScientistDesign and analyze batching algorithm experiment
This problem tests the ability to design a cluster-randomized experiment in a marketplace setting, focusing on managing network effects (spillover) and defining appropriate inference strategies for geo-based batches.
Analytics & ExperimentationOnsiteExperimentationCausal InferenceMarketplace - DoorDashHardData ScientistDesign and evaluate an uplift model
This question evaluates competency in causal inference and uplift modeling, experimental design for randomized treatment allocation, offline evaluation of uplift models, operational decision rules under budget constraints, fairness guardrails, and handling interference and deployment of sequential tests in a data-science context.
Machine LearningOnsite - DoorDashHardSoftware EngineerDesign consistent hashing for sharding
This question evaluates understanding of consistent hashing, replica placement, dynamic membership, weighted load distribution, rebalancing impact, and related distributed-systems competencies for routing and sharding.
System DesignOnsite - DoorDashMediumSoftware EngineerDesign dasher-to-order assignment algorithm
This question evaluates a candidate's skills in real-time algorithm design and systems thinking for dynamic matching and optimization, including scalability, time/space complexity analysis, and handling partial or delayed information.
Coding & AlgorithmsOnsite - DoorDashMediumSoftware EngineerDesign dynamic connectivity with alive nodes
This question evaluates understanding of dynamic graph data structures, connectivity algorithms, state management for node activation/deactivation, complexity analysis, and concurrent operation considerations.
Coding & AlgorithmsOnsite - DoorDashHardData ScientistDesign experiments for marketplace product changes
This question evaluates experiment design, causal inference, product-analytics, and metric-definition skills for multi-sided marketplaces, focusing on handling interference, spillovers, heterogeneity, and trade-offs between conversion, retention, credit burn, and monetization.
Analytics & ExperimentationOnsite - DoorDashEasyData ScientistDesign experiments for payments, search, and promotions
This question tests your ability to design robust A/B tests for marketplace products, focusing on metric selection, identifying interference, and accounting for ecosystem-level trade-offs.
Analytics & ExperimentationOnsiteA/B TestingMarketplace AnalyticsCausal Inference - DoorDashHardSoftware EngineerDesign review system with voting and rewards
This system design question evaluates a candidate's ability to architect a scalable review platform with voting, counter aggregation, and rewards at high traffic volumes. It tests practical knowledge of distributed counter strategies, idempotent write handling, read/write path separation, and abuse prevention — core competencies assessed in senior software engineer interviews.
System DesignOnsite - DoorDashMediumData ScientistDiagnose Cold Food Deliveries with Key Metrics Analysis
DoorDash analytics prompt on diagnosing cold food deliveries, covering complaint metrics, lifecycle timestamps, batching, packaging, weather, segmentation, data quality, A/B testing, and operational guardrails.
Analytics & ExperimentationOnsite
DoorDash