Skip to contentSkip to footer

Machine Learning Engineer Interview Questions

Salaries
Interviews

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.

8,203 machine learning engineer interview questions shared by candidates

Questions related around my current work and in depth dive into the tools I've been using to orchestrate machine learning pipelines. Since Slalom is a consulting company, they are cloud agnostic. I was more familiar with GCP. What is Vertex AI? What limitations do you see in Vertex AI? How would you create a pipeline in Vertex AI? I think Vertex AI is GCP's service similar to AWS Sagemaker but i might be wrong.
avatar

Machine Learning Engineer

Interviewed at Slalom

3.5★
Aug 20, 2021

Questions related around my current work and in depth dive into the tools I've been using to orchestrate machine learning pipelines. Since Slalom is a consulting company, they are cloud agnostic. I was more familiar with GCP. What is Vertex AI? What limitations do you see in Vertex AI? How would you create a pipeline in Vertex AI? I think Vertex AI is GCP's service similar to AWS Sagemaker but i might be wrong.

Each day a quarry-worker is given a pile of stones and told to reduce the larger stones into smaller ones. The worker must smash the stones together to reduce them, and is told to always pick up the largest two stones and smash them together. If the stones are of equal weight, they both disintegrate entirely. If one is larger, the smaller one is disintegrated and the larger one is reduced by the weight of the smaller one. Eventually, there is either one stone left that cannot be broken, or all of the stones have been smashed. Determine the weight of the last stone, or return O if there is none. Example weights = [1,2,3,6,7,7]. The worker always starts with the two largest stones. In this case, the two largest stones have equal weights of 7 so they both disintegrate when smashed. Next the worker smashes weights 3 and 6. The smaller one is destroyed and the larger weighs 6 - 3 = 3 units. Then, weights 3 and 2 are smashed together, which leaves a stone of weight 1. This is smashed with the last remaining stone of weight 1. There are no stones left, so the remaining stone weight is 0. Function Description Complete the function lastStoneWeight in the editor below. The function must return an integer that denotes the weight of the last stone, or 0 if all stones shattered into dust. lastStoneWeight has the following parameter(s): int weights[n]: an array of integers indicating the weights of each stone Constraints • 1 5n≤ 105 • 1 ≤ weights[i] ≤ 109
avatar

Senior Machine Learning Scientist

Interviewed at Wayfair

3.1★
Sep 3, 2024

Each day a quarry-worker is given a pile of stones and told to reduce the larger stones into smaller ones. The worker must smash the stones together to reduce them, and is told to always pick up the largest two stones and smash them together. If the stones are of equal weight, they both disintegrate entirely. If one is larger, the smaller one is disintegrated and the larger one is reduced by the weight of the smaller one. Eventually, there is either one stone left that cannot be broken, or all of the stones have been smashed. Determine the weight of the last stone, or return O if there is none. Example weights = [1,2,3,6,7,7]. The worker always starts with the two largest stones. In this case, the two largest stones have equal weights of 7 so they both disintegrate when smashed. Next the worker smashes weights 3 and 6. The smaller one is destroyed and the larger weighs 6 - 3 = 3 units. Then, weights 3 and 2 are smashed together, which leaves a stone of weight 1. This is smashed with the last remaining stone of weight 1. There are no stones left, so the remaining stone weight is 0. Function Description Complete the function lastStoneWeight in the editor below. The function must return an integer that denotes the weight of the last stone, or 0 if all stones shattered into dust. lastStoneWeight has the following parameter(s): int weights[n]: an array of integers indicating the weights of each stone Constraints • 1 5n≤ 105 • 1 ≤ weights[i] ≤ 109

Viewing 561 - 570 interview questions

Glassdoor has 8,203 interview questions and reports from Machine learning engineer interviews. Prepare for your interview. Get hired. Love your job.