Nextdoor Interview Questions — Page 2

Nextdoor·Software Engineer·Technical Phone Screen

May 2026

Nextdoor analytics engineering interview, SQL-heavy round focused on building idempotent ETL pipelines on top of rollup tables. The question was a continuation of an earlier dashboard design problem, so if you haven't thought through the schema beforehand you're already behind.

Given raw tables for users, sessions, photos, likes, comments, and follows, write idempotent SQL ETL queries that compute and upsert KPIs (user growth, DAU, photo activity, and follower distribution including median and p90) into rollup tables for a target date parameter.

“This one took me a while to even parse.”

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Nextdoor·Machine Learning Engineer·Technical Phone Screen

May 2026

Nextdoor ML Engineer interview with a coding question around interval merging. Pretty standard algorithmic problem but the string parsing and day-of-week ordering added some friction I didn't fully anticipate going in.

Given a list of meeting time intervals as day-and-time strings in 24-hour format, merge all overlapping intervals per day and return the results sorted by day of week then start time.

“The core merge logic wasn't the hard part.”

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Nextdoor·Software Engineer·Technical Phone Screen

May 2026

Nextdoor coding round, basically a Merge Intervals variant but with weekly calendar strings instead of plain numbers. The twist kept it interesting but also added a lot of places to mess up.

Given a list of time intervals expressed as string pairs in the format 'DAY HH:MM' (where DAY is Mon through Sun), merge all overlapping or adjacent intervals and return the result sorted chronologically. Intervals where the end comes before the start should be treated as wrapping past Sunday into the following Monday, so split them into two sub-intervals before merging.

“The core merge logic wasn't the hard part, I've done LC 56 before.”

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Nextdoor·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Coding round for an ML Engineer role at Nextdoor. The problem was a feed construction question that felt more like a backend/data engineering puzzle than anything ML-specific, which threw me a bit.

Given a list of feed objects each with an id, type (Normal, Video, or Photo), and a numeric score, implement a function that constructs a final ranked feed by sorting objects by score descending, grouping consecutive Photos into batches of up to 3, treating each batch as a single feed item scored by its first photo, then merging everything back into a final descending-score order.

“The sorting part was fine, stable sort by score and move on.”

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Nextdoor·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Interviewed for a PM role at Nextdoor and got a product strategy question about building a product-led growth approach. Pretty open-ended, which I wasn't totally ready for.

How would you build an effective product-led growth strategy?

“I started talking about activation and retention loops but realized mid-answer I wasn't tying it to Nextdoor specifically at all.”

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Nextdoor·Mobile Engineer·Technical Phone Screen

Jun 2026

Nextdoor mobile engineer interview with a tree/graph traversal problem centered on flattening nested comment threads. The follow-ups pushed into iterative rewriting which is where things got interesting.

Given a post with nested comment threads where each comment can have child comments at arbitrary depth, flatten the entire structure into a single array using depth-first search.
How would you modify your solution to stop traversal beyond a certain depth level?
Rewrite your recursive DFS solution iteratively using an explicit stack instead of recursion.

“My recursive solution came out clean enough but I fumbled explaining why DFS specifically made sense here versus BFS.”

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Nextdoor·Software Engineer·Technical Phone Screen

Jun 2026

Nextdoor software engineering interview that was basically a full ML pipeline exercise in Colab. Dense question covering a lot of ground at once, felt more like a take-home crammed into a live session.

Design and implement an end-to-end classification workflow on a tabular dataset in Google Colab. Cover data loading, EDA, preprocessing (missing values, scaling, encoding), train/validation split, model selection from baseline to stronger models, hyperparameter tuning, and evaluation with justified metrics. Also address class imbalance, data leakage prevention, cross-validation, and confidence interval reporting.

“This was a lot to hold in your head at once.”

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Nextdoor·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Nextdoor ML Engineer interview that was basically a version string sorting problem dressed up with a bunch of edge cases they clearly wanted you to sweat through. One coding round, algorithm-heavy, with a complexity discussion tacked on at the end.

Given up to 100,000 version-like strings (e.g. '1.0', '01.2.0', '2.0.0-alpha', '1.10.3'), implement a comparator that orders them by splitting on '.', comparing numeric segments as integers, treating missing segments as 0, sorting pre-release tags like '-alpha' and '-beta' before the corresponding release, and ignoring numeric build metadata after '+'. Use the comparator to return the sorted list. Also discuss time/space complexity and edge cases like leading zeros, different segment counts, and non-numeric segments.

“I started with the segment splitting and integer parsing, which felt fine, then the pre-release ordering tripped me up for a bit.”

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Nextdoor·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Nextdoor ML Engineer interview that was essentially a full coding exercise: given a raw DataFrame, implement data prep, train a model from scratch with numpy, evaluate it, and then field a bunch of concept questions. No sklearn allowed, which is where things got real.

Given a pandas DataFrame with numeric and categorical features and a target column, how would you handle missing values, encode categoricals, split the data, and standardize features without using any ML libraries?
Implement logistic regression or linear regression from scratch using numpy, including gradient descent training and a prediction function.
What evaluation metrics would you choose for a regression task versus a binary classification task, and how would you detect and address overfitting?
Explain the bias-variance tradeoff and how it relates to model complexity.
How does L1 regularization differ from L2, and what does each one do to the loss objective?
How would you handle class imbalance in a binary classification problem?
When does feature scaling matter and why? What happens if you skip it?
What is train/validation/test leakage and how do you prevent it during data preparation?

“The no-sklearn constraint hit harder than expected.”

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Nextdoor·Machine Learning Engineer·Take-home Assignment

Jun 2026

Nextdoor ML engineer take-home where you build a full classification pipeline in Colab on a tabular dataset they provide. Pretty standard end-to-end exercise but they actually want you to talk through your decisions live, not just hand in a notebook with good numbers.

Build an end-to-end classification model on a provided tabular dataset in a shared notebook environment. Cover data exploration, preprocessing, modeling, evaluation, and iteration while explaining your decisions out loud.

“The part that tripped me up was the 'communicate as you go' expectation.”

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