Katkat

Company

Nextdoor interview questions

21 practice questions in the style of Nextdoor's interviews, covering Behavioral & Leadership, Coding & Algorithms, Machine Learning, ML System Design, System Design, Software Engineering Fundamentals, Analytics & Experimentation.

Questions21

1 easy15 medium5 hard

Your progress0/21

Nextdoor interview questions by the numbers

Last updated

Katkat has 21 Nextdoor interview questions. The largest topics are Coding & Algorithms (43%), System Design (24%) and Behavioral & Leadership (14%). By difficulty: 5% easy, 71% medium and 24% hard. The most common round is Technical Screen (90%). Frequent concepts include Data Structures, Design, Distributed Systems and Simulation.

Difficulty

  • Easy5% · 1
  • Medium71% · 15
  • Hard24% · 5

Interview rounds

  • Technical Screen90% · 19
  • HR Screen10% · 2

Roles

  • Software Engineer43% · 9
  • Machine Learning Engineer33% · 7
  • Data Engineer14% · 3
  • Data Scientist5% · 1
  • iOS Engineer5% · 1

Frequent concepts

  • Data Structures10% · 2
  • Design10% · 2
  • Distributed Systems10% · 2
  • Simulation10% · 2
  • System Design10% · 2
  • A/B Testing5% · 1

Showing 1–21 of 21 results

  • This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Answer HR motivation and project experience questions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    NextdoorMachine Learning EngineerBehavioral & LeadershipTechnical Screen
  • This question evaluates algorithmic reasoning and data manipulation skills, including stable sorting, grouping/batching, and merging of ranked items while preserving ordering invariants.

    NextdoorMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in applied machine learning competencies including data preprocessing, categorical encoding, feature scaling, implementing baseline models and optimization using numpy/pandas, and selecting appropriate evaluation metrics.

    NextdoorMachine Learning EngineerMachine LearningTechnical Screen
  • This question evaluates a candidate's competency in building end-to-end tabular classification pipelines, including data loading and splitting, missing-value handling, categorical encoding, feature scaling, model training and comparison, hyperparameter tuning, metric-based evaluation, model persistence, and batch inference.

    NextdoorMachine Learning EngineerML System DesignTechnical Screen
  • This question evaluates practical and theoretical competence in the CAP theorem, distributed system architecture, and multithreaded concurrency, covering consistency models, partition tolerance, fault tolerance, scaling strategies, synchronization primitives, and debugging/performance techniques.

    NextdoorSoftware EngineerSystem DesignHR Screen
  • Describe resume highlights and confirm logistics evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    NextdoorMachine Learning EngineerBehavioral & LeadershipTechnical Screen
  • Tests your ability to design an in-memory key-value store supporting nested transactions, named savepoints, and reliable rollback semantics using undo logs or snapshotting.

    NextdoorSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • This Nextdoor data engineer system design screen asks you to design the analytical data model and incremental ETL behind a fast, large-scale KPI dashboard for a photo-sharing app. It covers derived aggregate table design (grain, keys, partitioning), DAU/new-user/new-photo and follower-distribution metrics, 7-day deltas, then extends to week-1 cohort retention, weekly rollups, and switching the reporting timezone. The focus is correctness, performance at scale, and maintainability with timezone handled as a reporting parameter.

    NextdoorData EngineerSystem DesignTechnical Screen
  • Design a distributed, asynchronous job scheduler that starts at modest traffic but can seamlessly scale up 100x. Evaluates API design, data storage trade-offs, worker coordination, task deduplication, and failure handling across scale boundaries.

    NextdoorSoftware EngineerSystem DesignTechnical Screen
  • Design a privacy-aware, hyperlocal social feed that serves real-time updates and emergency alerts based on strict neighborhood boundaries.

    NextdoorSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates system design skills focused on job scheduling, distributed systems concerns (state modeling, retries, idempotency), API and data-model design, fault tolerance, observability, and scalability within the System Design domain.

    NextdoorSoftware EngineerSystem DesignTechnical Screen
  • This problem tests your ability to traverse a nested tree structure (comments) and flatten it into a linear representation while respecting hierarchical depth levels.

    NextdooriOS EngineerCoding & AlgorithmsTechnical Screen
  • Implement a feed transformation that collapses consecutive photo posts by the same user into album items and then stably sorts the resulting feed by timestamp.

    NextdoorSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of dependency resolution and ordering within graph-based problems, including cycle detection, lexicographic tie-breaking, and extensions requiring certain items to appear as contiguous groups.

    NextdoorMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates string parsing and comparison algorithms, handling of numeric versus lexical ordering and versioning semantics (including pre-release identifiers and ignored build metadata), within the Coding & Algorithms domain.

    NextdoorSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to design an experimentation framework for a localized product launch and identify potential confounding factors and interference effects.

    NextdoorData ScientistAnalytics & ExperimentationTechnical Screen
  • This question evaluates the ability to parse weekday and 24-hour time strings, reason about interval overlap, and apply efficient merging and sorting techniques for time-based intervals.

    NextdoorMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • Implement an in-memory lock manager simulating shared and exclusive lock scheduling with strict FIFO queuing to prevent starvation.

    NextdoorSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates self-awareness, cultural fit, reflective leadership, and communication skills within the Behavioral & Leadership domain for a software engineering role.

    NextdoorSoftware EngineerBehavioral & LeadershipHR Screen
  • This question evaluates proficiency in SQL-based data engineering, focusing on time-zone-aware timestamp handling, aggregation of event and relationship data, cohort retention analysis, and computation of product metrics like DAU, new users, photo activity, and follower distributions.

    NextdoorData EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in SQL for relational databases, specifically testing aggregation, joins, date/time grouping, and handling tied results when computing user metrics such as total users, peak signup month, and top-followed users.

    NextdoorData EngineerCoding & AlgorithmsTechnical Screen

Nextdoor interview experiences

All 1 →
  • Solved Both Screens, Then RejectedNextdoor · Software Engineer · Senior+ · Technical Screen · Sep 2026
    Rejected

    This was for a Senior general SDE role, so there were two phone screens. Both were known questions. I had prepared every question in the question bank, written each one out once, and prepared all the follow-ups. I don't think there should have been any problems with my answers. Merge weekday time intervals: I could define the input and output formats myself. I chose ["Mon 09:00", "Mon 17:00"]. In…

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