DoorDash interview questions

Showing 91–120 of 241 results

• Design a Top Dasher experiment with interferenceEasy

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design a distributed cron job schedulerHard

  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.

  DoorDashSoftware EngineerSystem DesignOnsite
• Design a donations service with 3-day rolling totalsMedium

  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.

  DoorDashSoftware EngineerSystem DesignOnsite
• Design a hierarchical path registryMedium

  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.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Design a multi-channel notification systemMedium

  This System Design question evaluates distributed-systems and API-design skills—scalability, reliable fanout, multi-channel integration, delivery guarantees (retries, idempotency, deduplication), rate-limiting, subscription/preferences modeling, and observability.

  DoorDashSoftware EngineerSystem DesignOnline Assessment
• Design a notification systemHard

  This question evaluates a candidate's competency in designing scalable, reliable multi-channel notification platforms, covering distributed systems, multitenancy, API and storage schema design, queuing and retry strategies, rate limiting, observability, localization, A/B testing, and compliance considerations like GDPR.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design a resilient bootstrap APIMedium

  This question evaluates skills in resilient API aggregation, fault-tolerant microservice orchestration, and reliability engineering, focusing on defining API contract semantics and handling partial failures at the architectural/system-design level (service-to-service abstraction); category/domain: System Design.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design a resilient dasher payment systemHard

  A DoorDash software-engineer system-design interview question: design a resilient Dasher payment system that computes payouts from order lifecycle events (accept/fulfill/cancel). It spans event-driven ingestion, idempotency and exactly-once-vs-at-least-once semantics, data modeling for high write throughput and per-dasher pay-period queries, timezone/DST handling, detection and remediation of missing or out-of-order data, batch-vs-streaming reconciliation, reliability, and consistency trade-offs.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design a scalable Instagram-like platformHard

  Design a scalable Instagram-like platform 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.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design a scalable recommendation serving systemMedium

  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.

  DoorDashMachine Learning EngineerSystem DesignOnsite
• Design a service aggregator with robust error handlingHard

  Design a service aggregator with robust error handling 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.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design a single-machine LRU cacheMedium

  This question evaluates a candidate's ability to design an efficient in-memory LRU cache and apply concurrency control, covering eviction semantics, capacity management, key updates, and synchronization for thread safety.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design an API for pay computation with retriesMedium

  Assesses the candidate's ability to design robust HTTP APIs covering endpoint modeling, request/response schemas, datetime formats, idempotency strategies, retry semantics, validation/error codes, and observability for pay-computation services.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnline Assessment
• Design an Alert Notification Service for Downstream ConsumersMedium

  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.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design an Online Course Marketplace Review System SchemaHard

  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.

  DoorDashSoftware EngineerSystem DesignOnsite
• Design an async donation payment platformHard

  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.

  DoorDashSoftware EngineerSystem DesignOnsite
• Design an experiment for order batchingHard

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design analysis to reduce cold-delivery complaintsHard

  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.

  DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
• Design and Evaluate a Refund Policy for Delayed OrdersMedium

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design and analyze a switchback experimentHard

  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.

  DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
• Design and analyze batching algorithm experimentHard

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design and evaluate an uplift modelHard

  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.

  DoorDashData ScientistMachine LearningOnsite
• Design consistent hashing for shardingHard

  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.

  DoorDashSoftware EngineerSystem DesignOnsite
• Design consistent hashing with a sorted mapMedium

  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.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Design dasher-to-order assignment algorithmMedium

  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.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Design dynamic connectivity with alive nodesMedium

  This question evaluates understanding of dynamic graph data structures, connectivity algorithms, state management for node activation/deactivation, complexity analysis, and concurrent operation considerations.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Design experiments for marketplace product changesHard

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design experiments for payments, search, and promotionsEasy

  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.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Design hierarchical path store with create/set/get/removeMedium

  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.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design limited-time donation platformHard

  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.

  DoorDashSoftware EngineerSystem DesignTechnical Screen