I am not sure whether I can go into the details. But it was general coding question for the first round. And for the second round questions were relevant to the role I applied for.
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.
8,205 machine learning engineer interview questions shared by candidates
What is your biggest weakness?
Asked me about GANs and machine learning in general
1. Optimization Equation for SVM 2. Implementation of Random Forest on demo dataset
introducing your self , your education , experience and projects you worked in
Q(R1): What is your past experience, and how would you fit the role we are hiring for? Q(R2): What is the difference between bagging and boosting? What algorithms do you know from each class, and on what types of problems have you applied them in the past? Q(R3): Given this data, how would you build the dataset and the ML training pipeline?
What is the difference between SVM and Logistic Regression?
Assessment based on the machine learning
Questions were about explaining decorators, inheretence etc
why do you want to join the company
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