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.

8,202 machine learning engineer interview questions shared by candidates

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).
avatar

Machine Learning Engineer

Interviewed at Meta

3.5
Aug 1, 2024

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).

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