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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 3001–3030 of 9403 results
- DoorDashMediumSoftware EngineerDesign an Alert Notification Service for Downstream Consumers
Design a reliable, scalable alert and event notification delivery platform that decouples upstream producers from heterogeneous downstream consumers with independent fan-out, isolation, retries, and dead-letter handling.
System DesignTechnical ScreenDistributed SystemsMessage QueuesEvent-Driven - 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 - DoorDashHardData ScientistDesign analysis to reduce cold-delivery complaints
This problem tests the ability to diagnose a service quality issue, define key performance indicators, and design an experiment to evaluate an operational intervention while controlling for confounding variables.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimental DesignProduct Metrics - 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 a switchback experiment
This question evaluates proficiency in experimental design, causal inference, randomization and contamination control, regression modeling with fixed effects and clustered standard errors, power calculation, and handling of compliance and robustness issues.
Analytics & ExperimentationTechnical Screen - 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 consistent hashing with a sorted map
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 consistent hashing with a sorted map states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - 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 - DoorDashMediumSoftware EngineerDesign hierarchical path store with create/set/get/remove
This question evaluates a candidate's competency in hierarchical data structures, path parsing, API semantics, error signaling, complexity analysis, and unit testing for implementing a UNIX-style key-value path store.
Coding & AlgorithmsTechnical Screen - DoorDashHardSoftware EngineerDesign limited-time donation platform
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 limited-time donation platform states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - DoorDashHardSoftware EngineerDesign notification and project architecture
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 notification and project architecture states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - DoorDashMediumMachine Learning EngineerDesign personalized restaurant search and recommendations
This question tests the ability to design an end-to-end personalized restaurant recommendation system, covering feature engineering, retrieval-ranking architecture, and infrastructure trade-offs.
System DesignTechnical ScreenRecommendation SystemsRankingSearch - 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 ScientistDetermine Success Metrics for Biker Dasher Program Launch
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 Success Metrics for Biker Dasher Program Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashMediumSoftware EngineerDetermine equality after limited swaps
This question evaluates string manipulation and combinatorial reasoning about character swaps and anagram formation, focusing on competencies such as frequency analysis and reasoning about minimal edit operations.
Coding & AlgorithmsTechnical Screen - 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 - DoorDashMediumData ScientistDiagnose Cold-Food Deliveries and Make a Launch Decision
Work through a product analytics case about diagnosing a delivery-quality problem and deciding whether an intervention should launch. The question tests metric hierarchy, causal reasoning, investigation design, power considerations, and decision criteria.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistDiagnose Decline in Successful Orders
This question evaluates a data scientist's skills in marketplace analytics, causal reasoning, metric framework design, and experimentation for diagnosing a decline in successful orders.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistDiagnose LA completed-order drop and design experiment
This question evaluates a data scientist's competency in funnel-based diagnostics, causal inference, and experimental design for distinguishing supply-side versus demand-side drivers in a two-sided marketplace, emphasizing metric selection, segmentation, and guardrail monitoring.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistDiagnose Why Delivered Food Arrives Cold
Investigate an increase in cold food complaints by decomposing fulfillment timestamps, evaluating mix shifts versus operational degradation, and designing a mitigation experiment with balanced marketplace guardrails.
Analytics & ExperimentationOnsiteanalyticsexperimentationfunnel-analysis - DoorDashMediumData ScientistDiagnose a Decline in Order Acceptance Rate
Evaluate a sudden drop in Dasher order acceptance rate by verifying data integrity, decomposing segment-level shifts, and designing an experiment to mitigate downstream marketplace impacts.
Analytics & ExperimentationOnsiteProduct AnalyticsMetric DecompositionA/B Testing - DoorDashMediumData ScientistDiagnose a One-Day Drop in Successful Deliveries
Tests the candidate's structured metric troubleshooting, funnel decomposition, and root cause analysis for an acute drop in core marketplace volume. Evaluates the ability to separate count effects from conversion rate changes and isolate partner-specific vs. market-wide anomalies.
Analytics & ExperimentationTechnical Screenmetric-troubleshootingfunnel-analysismarketplace-analytics - DoorDashHardData ScientistDiagnose and experiment to reduce late deliveries
This question evaluates a data scientist's competencies in causal inference, metrics engineering, data instrumentation and joining, root‑cause analysis, and experiment design focused on operational delivery performance.
Analytics & ExperimentationTechnical Screen
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