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Nextdoor interview notes, page 1 of 3

Mid-sized

Most of these Nextdoor write-ups come from Software Engineers (14), then Machine Learning Engineers (12) and Mobile Engineers (2). The most reported rounds are the technical phone screen (19), the system design round (4), the recruiter screen (2) and the take-home assignment (2).

29 interview notes · updated Jul 2026

Nextdoor·Mobile Engineer·Technical Phone Screen

Apr 2026
Nextdoor mobile engineering interview focused on a UIKit debugging exercise covering a pretty wide range of iOS-specific issues. Not a leetcode grind, more of a 'here's broken code, fix it and talk through your reasoning' kind of session.
  • Given a UIKit iOS project with multiple bugs, identify and fix issues related to object lifecycle and scope management.
  • Identify and resolve threading issues in the provided iOS codebase.
  • Find and fix the table view cell sizing bugs in the project.
  • Debug and correct broken button action handling in the UIKit project.
  • How would you improve the overall code quality, modularity, and architecture of this iOS project?

“This tripped me up more than it should have.”

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Nextdoor·Software Engineer·Technical Phone Screen

Apr 2026
Nextdoor analytics engineer interview, SQL-heavy technical screen. Three queries on a social graph schema plus a discussion about indexing strategy. Pretty standard for the role but the indexes conversation is where they actually push back.
  • Write a SQL query to return the total number of users in the database.
  • Write a SQL query to find the calendar month with the highest number of user signups, returning the month and the count.
  • Write a SQL query to find the user with the most followers, returning their user_id and follower count. Handle ties in any consistent way.
  • What indexes would improve performance for each of these three queries, and why?

“Warmup question, COUNT(*) on users, nothing to it.”

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Nextdoor·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Nextdoor software engineer interview with a culture-fit component that required watching a company video beforehand and then defending which principle stuck with you. Felt a bit unusual compared to standard behavioral rounds.
  • After watching Nextdoor's culture-fit video, which principle resonated most with you and why? Back it up with a concrete example from your past work, and share anything you'd push back on or change about that principle.

“The 'what would you challenge' part is where I stumbled.”

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Nextdoor·Machine Learning Engineer·Recruiter / HR Screen

Apr 2026
Recruiter screen for a Machine Learning Engineer role at Nextdoor. Pretty standard stuff, mostly resume walkthrough and logistics. Nothing technically deep but it set the tone for what they care about.
  • Walk me through 2 or 3 projects from your resume, covering your specific role, the technical decisions you made, and any measurable results.
  • What is your earliest available start date, work authorization status, location preference (onsite, hybrid, or remote), and are there any time zone restrictions we should know about?

“I picked projects I thought were impressive but realized mid-answer I hadn't quantified the impact well for one of them.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Nextdoor for a software engineer role. Just one question but it took the full hour and there was a lot of ground to cover.
  • Design the newsfeed system for Nextdoor.

“Spent too long on the data model upfront and had to rush through the feed ranking and delivery parts.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Nextdoor for a software engineer role. The whole thing was one big question about building a job scheduler, starting simple and then scaling it up 100x. Felt like a fair problem but there were a few corners I didn't handle as cleanly as I'd have liked.
  • Design a job scheduler for a small startup that supports one-time and recurring jobs, retries, cancellation, state tracking, and basic observability. Then walk through how you'd evolve it to handle roughly 100x the load.
  • How would you handle duplicate job execution and ensure idempotency across workers?
  • What happens to scheduled jobs if the scheduler node itself crashes? How do you recover?
  • Walk me through your API design and core data model for this scheduler.

“I started with a simple postgres table for job storage and a polling loop to pick up pending jobs, which felt fine for the startup phase.”

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Nextdoor·Software Engineer·Technical Phone Screen

May 2026
SQL round for a Data Engineer role at Nextdoor, built around a fake social app schema. Three questions, all relational, nothing crazy but the last one had a small wrinkle I didn't fully anticipate.
  • Using the users table, write a query to count the total number of users.
  • Which month saw the highest number of user signups? Return both the month and the count.
  • From the follows table, find the username of the user with the most followers. If there's a tie, return all tied users.

“Warmup question, basically just COUNT(*) FROM users.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026
Data engineering interview at Nextdoor centered entirely on one massive analytical modeling question. It was a multi-part beast covering table design, ETL strategy, retention, rollups, and timezone handling. Felt more like a take-home that got crammed into a live session.
  • Design the derived table schema that powers a fast daily KPI dashboard for a large photo-sharing app. Cover grain, keys, what gets precomputed versus computed at query time, and how you'd handle partitioning, clustering, and incremental loads.
  • Walk through the daily ETL process to populate these KPI tables from the raw source tables. High-level steps are fine, SQL or pseudocode is acceptable.
  • How would you add week-1 retention to this dashboard? For each signup cohort date, what fraction of users are active exactly one week later? What table changes are needed and how do you compute it efficiently?
  • The dashboard needs to support weekly rollups instead of daily. What changes in the table design and computation, particularly around non-additive metrics, partial weeks, and how 'change vs 7 days ago' translates to a weekly grain?
  • A New York office wants the same metrics reported in Eastern time instead of Pacific. What needs to change across timestamp handling, ETL windows, backfills, and how do you support both timezones without double-counting events at day boundaries?

“This is where I spent most of my energy and still felt like I was only halfway there.”

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Nextdoor·Machine Learning Engineer·Technical Phone Screen

May 2026
Coding round for an MLE role at Nextdoor. One problem, but it had enough edge cases to keep me busy for the whole session.
  • You have a feed of objects, each with an id, score, and type (Normal, Video, or Photo). Group every 3 consecutive Photo objects into a single grouped photo object whose score is the max score among the three. Then sort all objects by score descending. How do you handle photo groups with fewer than 3 photos, and how does the grouping interact with the score-based sort?

“The basic grouping pass felt fine, sliding through the list and collecting photos as I hit them.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026
Nextdoor software engineer interview that leaned heavily on distributed systems depth. Three meaty questions back to back, all requiring you to draw from real production experience rather than textbook answers.
  • Explain the CAP theorem and walk through a real system you designed: which two properties did you prioritize during a network partition, what trade-offs came with that choice, and why did you make it?
  • Describe your hands-on experience building and running distributed systems: what architectures did you use, what were the hardest problems you hit around consistency, fault tolerance, scaling, or observability, and how did you actually solve them?
  • Talk through your experience with multithreaded programming: what synchronization primitives have you used, how did you prevent deadlocks and data races, how did you debug concurrency bugs, and what did you do to improve performance?

“The 'explain CAP' part is easy enough but they immediately pushed into a real example, which is where it gets uncomfortable.”

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