Machine Learning Engineer Interviews

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.

8,202 machine learning engineer interview questions shared by candidates

Round 2) Some questions that I remember: Explain the programming assignments, why are the weights randomly initialized, what is an activation function, what is the difference between SoftMax and ReLU activation, what are hyperparameters, list down all the hyperparameters, what are color channels, why do we convert images to grayscale, how does. Otsu's image segmentation work, what is the size of the image before and after converting to grayscale.
avatar

Machine Learning Intern

Interviewed at Neva Ventures

4.4
Aug 24, 2018

Round 2) Some questions that I remember: Explain the programming assignments, why are the weights randomly initialized, what is an activation function, what is the difference between SoftMax and ReLU activation, what are hyperparameters, list down all the hyperparameters, what are color channels, why do we convert images to grayscale, how does. Otsu's image segmentation work, what is the size of the image before and after converting to grayscale.

Round 3) Some questions that I remember: Explain academic projects, how does an Artificial Neural Network work, what is feed-forward and backward propagation, what is gradient descent, what is the difference between global minimum and local minima, how do you avoid local minima, what is a parameter, difference between parameter and hype parameter, list down each parameter and hyperparameter, mathematical questions on loss function, what is overfitting, how do you avoid overfitting, what is regularization, where do you add regularization term, questions on image classification using CNN, the question to find the second largest element of an array, and a mathematical puzzle.
avatar

Machine Learning Intern

Interviewed at Neva Ventures

4.4
Aug 24, 2018

Round 3) Some questions that I remember: Explain academic projects, how does an Artificial Neural Network work, what is feed-forward and backward propagation, what is gradient descent, what is the difference between global minimum and local minima, how do you avoid local minima, what is a parameter, difference between parameter and hype parameter, list down each parameter and hyperparameter, mathematical questions on loss function, what is overfitting, how do you avoid overfitting, what is regularization, where do you add regularization term, questions on image classification using CNN, the question to find the second largest element of an array, and a mathematical puzzle.

The telephone interview was reasonable was about Machine learning, Estimation theory, software engineering and C++ coding. The on-site interview was a bit biased to software engineering. There was no question about machine learning and it was solely C++11 , design patterns and white board coding. The questions like - name design patterns and show how to use one of them on the board - difference between modern c++ and the classical - what is rule of 5 etc The white board question: - given a text document write a c++ code to retrieve a text
avatar

Machine Learning Engineer

Interviewed at BMW Group

4.2
May 20, 2019

The telephone interview was reasonable was about Machine learning, Estimation theory, software engineering and C++ coding. The on-site interview was a bit biased to software engineering. There was no question about machine learning and it was solely C++11 , design patterns and white board coding. The questions like - name design patterns and show how to use one of them on the board - difference between modern c++ and the classical - what is rule of 5 etc The white board question: - given a text document write a c++ code to retrieve a text

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