Basic ML things such as loading images from disk, normalizing them, passing them through a pre-trained model, computing embeddings etc.
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.
8,212 machine learning engineer interview questions shared by candidates
BERT, OCR, NAtural Language Processing, Deeping Learning.
There were no questions being asked.
Interview questions are fine. But the unprofessionalism left a strong impression on me.
If there were a buh, how would you deal with them
Bagging vs Boosting.Bias Variance tradeoff.
R1: It was a python test. they had given me table in consisting the information about all tests given by students. each entry in data was representing the test score. so I had to group them using pandas and pivot them by subject wise. I wrote the solution anyhow. then interview was based on basic of python and my solution. Round2: After clearing the round1 i got invitation for the r2 i.e NLP round. this time they had given me the dataset of customer problems and sub problems. I had to train a NLP model that can classify the type of problems into different category. I was learning NLP at that stage so I could not perform well in R2. but yeah I learned alot in these process.
Working of Autoencoders, VAEs, GANs etc.
Questions regarding specific machine learning models that I have never heard of before
The difference between a python list and tuple.
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