I applied online. The process took 1 week. I interviewed at Yandex (Moskau, )
Interview
I applied through the company's website. The HR manager got in touch with me almost immediately. After a 10-minute phone screen I was invited to an on-site tech interview.
The interview comprised two 1:1 sections with two different engineers, each section spanning 1 hour, with no pause in between. The first section was dedicated to probability and statistics. I was given two problems on conditional probability and Bayes rule, followed by a problem on statistics that required knowledge of confidence intervals, the central limit theorem etc. The second section focused on analytical and coding skills. I was offered a couple of brain teasers and then was asked to write some Python on a sheet of paper.
The interviewers were polite and professional but appeared somewhat too detached.
Due to my poor preparation I had trouble with going smoothly beyond the easiest problems. The written rejection came the same day.
Interview questions [2]
Question 1
Design a function that outputs N most frequent words in a given (large) text file.
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
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
I applied through a recruiter. The process took 3 months. I interviewed at Yandex (Moskau, ) 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.
