Machine Learning Engineer Intern Interview Questions

8,199 machine learning engineer intern interview questions shared by candidates

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).
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Machine Learning Engineer

Interviewed at Meta

3.5
Aug 1, 2024

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).

An initial round (background/ profile based), a simple classification problem as homework, a technical interview (mostly based on how the solution was framed for the homework), an interview with a non-technical person, and a final round with their co-founder. 1. Why does multicollinearity happen in regression? 2. Working of boosted trees 3. Types of regularization 4. Overfitting and underfitting
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Machine Learning Engineer

Interviewed at Quantiphi

3.8
Jun 21, 2019

An initial round (background/ profile based), a simple classification problem as homework, a technical interview (mostly based on how the solution was framed for the homework), an interview with a non-technical person, and a final round with their co-founder. 1. Why does multicollinearity happen in regression? 2. Working of boosted trees 3. Types of regularization 4. Overfitting and underfitting

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