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Machine Learning Engineer Interviews

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Machine Learning Engineer Interview Questions

Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.

Top Machine Learning Engineer Interview Questions & How to Answer

Question 1

Question #1: What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?

How to answer
How to answer: Be prepared to talk about things like Type I and Type II errors, supervised and unsupervised machine learning, ROC curves, and other key parts of machine learning. Employers want to know you have a strong knowledge of the technical aspects of the job position.
Question 2

Question #2: How would you explain machine learning to someone who doesn't understand it?

How to answer
How to answer: Sometimes machine learning engineers have to work with people who aren't familiar with the technical aspects of the job. Use this interview question as an opportunity to show your strong knowledge of the position and your communication abilities.
Question 3

Question #3: How do you stay up to date with the latest news and trends in machine learning?

How to answer
How to answer: By talking about how you're up to date with the latest news and trends in machine learning, you can show an employer that you're engaged in the industry, a skilled researcher, and self-motivated.

8,212 machine learning engineer interview questions shared by candidates

You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.
avatar

Machine Learning Scientist

Interviewed at Varian Medical Systems

3.9★
Dec 28, 2021

You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.

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