What's the difference between discriminative and generative machine learning?
Machine Learning Engineer Interviews
Machine Learning Engineer Interview Questions
Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
Top Machine Learning Engineer Interview Questions & How to Answer
Question 1
Question #1: What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?
How to answer
How to answer: Be prepared to talk about things like Type I and Type II errors, supervised and unsupervised machine learning, ROC curves, and other key parts of machine learning. Employers want to know you have a strong knowledge of the technical aspects of the job position.
Question 2
Question #2: How would you explain machine learning to someone who doesn't understand it?
How to answer
How to answer: Sometimes machine learning engineers have to work with people who aren't familiar with the technical aspects of the job. Use this interview question as an opportunity to show your strong knowledge of the position and your communication abilities.
Question 3
Question #3: How do you stay up to date with the latest news and trends in machine learning?
How to answer
How to answer: By talking about how you're up to date with the latest news and trends in machine learning, you can show an employer that you're engaged in the industry, a skilled researcher, and self-motivated.
8,198 machine learning engineer interview questions shared by candidates
optimization techniques (SMO, sgd, newton), momentum, saddle point good for sgd? , svm c change, conv or fcn layer has more memory ?
Q: Implement a function to check if a binary tree is balanced. Q: How would you evaluate the performance of a recommendation system, like the ones used on Amazon?
Behavioral questions during the online assessment.
LeetCode questions are at the easy/medium level. System design is a regular task about designing ML algorithms
Two leetcode questions (check description)
The interview questions were some behavioral ones, then some fundamental questions about machine learning concepts and definition of different algorithms, how to select one method over another one considering the data. The next phase was coding interview with two questions, some tricky questions also asked.
Text methods (latent, etc), he asked if I knew anything about these.
Describe an example where you had multiple alternatives to choose from for tools/approaches for a project. How did you go forward?
gradient descent
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