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      Machine Learning Engineer Interview

      6 Mar 2026
      Anonymous interview candidate
      No offer
      Neutral experience
      Difficult interview

      Application

      I interviewed at Nextdoor

      Interview

      Round 1: Backend Coding(1h) + ML coding(1h) Backend Coding: A non-leetcode question with regard to hashmap and recursion ML coding: Given a dataset, build a pipeline from data preprocessing to model training to evaluation

      Interview questions [1]

      Question 1

      A project that I'm most proud of
      Answer question

      Other Machine Learning Engineer interview reviews for Nextdoor

      Machine Learning Engineer Interview

      16 May 2026
      Anonymous interview candidate
      No offer
      Neutral experience
      Difficult interview

      Application

      I applied online. I interviewed at Nextdoor in May 2026

      Interview

      first is hr call 30 mins, then 1h general coding and 1h ml coding, if passing, there will be another 1.5h interview for genereal coding and hebavior question. The hr call is mainly for introducing the interview process and checking interest,

      Interview questions [1]

      Question 1

      Introduce an experience you proud of
      Answer question

      Machine Learning Engineer Interview

      25 Apr 2026
      Anonymous interview candidate
      Declined offer
      Positive experience
      Average interview

      Application

      I interviewed at Nextdoor

      Interview

      ⸻ Tree traversal questions were mostly LeetCode medium level, focusing on classic BFS and DFS patterns without heavy backtracking. The emphasis was on implementing standard traversals cleanly, understanding when to use iterative vs recursive approaches, and handling common variations like level-order processing or simple tree properties. The ML and system design portion focused primarily on recommender systems, along with fundamental machine learning theory. This included discussing how to design a recommendation pipeline end-to-end, tradeoffs between different approaches, and demonstrating a solid grasp of core concepts like model evaluation, overfitting, and basic algorithms.

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