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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.
1 easy15 medium5 hard
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.
Topics
- Coding & Algorithms43% · 9
- System Design24% · 5
- Behavioral & Leadership14% · 3
- Machine Learning5% · 1
- ML System Design5% · 1
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 ScreenThis 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 ScreenThis 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 ScreenThis 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 ScreenThis 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 ScreenDescribe 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 ScreenTests 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 ScreenThis 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 ScreenDesign 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 ScreenDesign a privacy-aware, hyperlocal social feed that serves real-time updates and emergency alerts based on strict neighborhood boundaries.
NextdoorSoftware EngineerSystem DesignTechnical ScreenThis 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 ScreenThis 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 ScreenImplement 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 ScreenThis 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 ScreenThis 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 ScreenThis 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 ScreenThis 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 ScreenImplement an in-memory lock manager simulating shared and exclusive lock scheduling with strict FIFO queuing to prevent starvation.
NextdoorSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates self-awareness, cultural fit, reflective leadership, and communication skills within the Behavioral & Leadership domain for a software engineering role.
NextdoorSoftware EngineerBehavioral & LeadershipHR ScreenThis 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 ScreenThis 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 2026Rejected
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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