Machine Learning Engineer Interview Questions

Machine Learning Engineer Interview Questions

Les entreprises s’appuient sur les machine learning engineers pour les aider à concevoir et à améliorer les systèmes qui permettent à leurs logiciels de s’améliorer eux-mêmes, plutôt que d’être programmés. Au cours de l’entretien, préparez-vous à être longuement interrogé sur vos connaissances en informatique et en science des données et, en particulier, sur votre capacité à reconnaître des modèles et des tendances. Un diplôme en informatique ou dans un domaine équivalent sera exigé.

Questions d'entretien d'embauche fréquentes pour un machine learning engineer (H/F) et comment y répondre

Question 1

Question 1 : Quels sont les algorithmes, termes de programmation et théories les plus importants à maîtriser en tant que machine learning engineer ?

How to answer
Comment répondre : Préparez-vous à parler de sujets tels que les erreurs de type I et de type II, l’apprentissage automatique supervisé et non supervisé, les courbes ROC et d’autres éléments clés de l’apprentissage automatique. Les employeurs veulent s’assurer que vous avez une solide connaissance des aspects techniques du poste à pourvoir.
Question 2

Question 2 : Comment expliquer l’apprentissage automatique à quelqu’un qui ne comprend pas ce domaine ?

How to answer
Comment répondre : Parfois, les machine learning engineers doivent travailler avec des personnes qui ne sont pas familières avec les aspects techniques du travail. Saisissez l’occasion que vous offre cette question pour montrer votre solide connaissance du poste et vos capacités de communication.
Question 3

Question 3 : Comment se tenir informé des dernières nouveautés et tendances en matière d’apprentissage automatique ?

How to answer
Comment répondre : En expliquant comment vous vous tenez au courant des dernières nouveautés et tendances en matière d’apprentissage automatique, vous pouvez montrer à un employeur que vous êtes engagé dans le secteur, que vous êtes un chercheur compétent et que vous êtes motivé.

8,208 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

Viewing 1081 - 1090 interview questions

Glassdoor has 8,208 interview questions and reports from Machine learning engineer interviews. Prepare for your interview. Get hired. Love your job.