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      Machine Learning Engineer Interview

      28 Jul 2024
      Anonymous interview candidate
      No offer
      Positive experience
      Average interview

      Application

      I applied online. I interviewed at Yandex

      Interview

      I recently interviewed for a Middle Machine Learning Engineer position at Yandex. Overall, the experience was insightful and challenging, providing a good sense of the company's technical depth and expectations. I got easy task from leetcode: 680. Valid Palindrome II

      Interview questions [1]

      Question 1

      leetcode task Valid Palindrome II
      Answer question
      2

      Other Machine Learning Engineer interview reviews for Yandex

      Machine Learning Engineer Interview

      10 Aug 2022
      Anonymous interview candidate
      Declined offer
      Negative experience
      Average interview

      Application

      I interviewed at Yandex

      Interview

      Awful questions about algorithms (not ml) and data structures, no sense in these type of questions. And awful tasks like old fashioned brainteasers. Not suitable for ML position. Only negative impression

      Interview questions [1]

      Question 1

      Basic data structures and algorithms
      Answer question

      Machine Learning Engineer Interview

      11 Jun 2023
      Anonymous interview candidate
      Moscow, Moskva
      Declined offer
      Positive experience
      Difficult interview

      Application

      I applied through a recruiter. The process took 3 months. I interviewed at Yandex (Moscow, Moskva) in Jan 2022

      Interview

      I recently had the opportunity to interview for a Machine Learning Engineer (MLE) position at Yandex Moscow, and I must say that the entire experience was exceptional. The MLE interview process at Yandex Moscow not only demonstrated the company's commitment to technical excellence but also reflected their dedication to fostering a positive and collaborative environment for candidates.

      Interview questions [1]

      Question 1

      - Explain the concept of overfitting in machine learning and describe techniques to mitigate it. - How would you approach feature selection and feature engineering for a given machine learning task? Provide examples of relevant features for a sentiment analysis problem. - Discuss the differences between supervised learning and unsupervised learning algorithms. When would you choose one over the other for a given problem? - Describe the working principles of convolutional neural networks (CNNs) and their applications in computer vision tasks. How do they handle spatial hierarchies and achieve translation invariance? - Suppose you are given a dataset with imbalanced classes for a binary classification problem. How would you address this issue and improve the performance of the model? Explain different techniques you can use, such as oversampling, undersampling, or cost-sensitive learning.
      Answer question
      2

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