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Machine Learning Research Engineer Interview Questions

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1,840 machine learning research engineer interview questions shared by candidates

In the actual interview; the first question is the closest common parents of two nodes in a binary tree given the parent node as a pointer in the node itself. And the second was the known file system path parsing problem. Both of these are leetcode
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Software Engineer - Machine Learning

Interviewed at Meta

3.6★
May 30, 2023

In the actual interview; the first question is the closest common parents of two nodes in a binary tree given the parent node as a pointer in the node itself. And the second was the known file system path parsing problem. Both of these are leetcode

Coding questions were medium leetcode questions and can be done with some prep. system design and ml system wasn't too hard. I couldn't do senior level on ml system design. it's more complicated than educative examples.
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Senior Machine Learning Engineer

Interviewed at Meta

3.6★
Aug 25, 2023

Coding questions were medium leetcode questions and can be done with some prep. system design and ml system wasn't too hard. I couldn't do senior level on ml system design. it's more complicated than educative examples.

Most of the questions were from my resume. Explain the criterion of choosing particular machine learning algorithm for the problems which I was trying to solve . Questions on deep learning algorithms and natural language processing. Questions were all about the different deep learning algorithm, like Autoencoder, seq2seq model, LSTM, CNN. The interviewer was interesting to know how I implemented the algorithms, what type of data set (numerical or text-based) data that I used, and the evaluation metric I used for my project.
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Machine Learning Engineer

Interviewed at Qualcomm

3.8★
Jul 2, 2017

Most of the questions were from my resume. Explain the criterion of choosing particular machine learning algorithm for the problems which I was trying to solve . Questions on deep learning algorithms and natural language processing. Questions were all about the different deep learning algorithm, like Autoencoder, seq2seq model, LSTM, CNN. The interviewer was interesting to know how I implemented the algorithms, what type of data set (numerical or text-based) data that I used, and the evaluation metric I used for my project.

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