If you get past the phone screen you will get a detailed, seemingly endless list of topics.
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.
8,199 machine learning engineer interview questions shared by candidates
Describe a project where things didn't work out as expected.
You have a linked list of numbers, how would you return the median ? Follow up, what is the worst case performance?
Behavioral question: do you have any experience of over-delivering the job?
Standard recruiter questions, nothing out of the blue.
How can u statistically interpret ridged regression?
Design question - Improve your performance and reliability in a given data pipeline.
How many balls are in a pool?
Tell me about a time you overcame a challenge at work.
I won't give details about the question as I respect the confidentiality of the interview. However, to give a general feeling, I think it doesn't hurt to mention the following. For example, code a class that implements a very popular ML algorithm. Even if the algorithm is very simple there are lots of possible improvements and generalisations, how to make it robust, efficient etc. Same thing for a class storing common data formats: dataframe, time-series, etc... how would you efficiently code access methods and/or storing according to the features of these data types?
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