Tensorflow, Python , Keras, NLP, SQL
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.
8,194 machine learning engineer interview questions shared by candidates
Under NDA, so no details. General motivation, but mostly technical: hard probability & statistics questions.
How do you solve overfitting?
Questions were from the 75 questions that everyone keeps mentioning on leetcode
Describe a time where you had to be resourceful to achieve a project outcome.
- live coding: write a python script to parse a log file, strip, splitting on string. Use of dictionary to aggregate data. Writing a automated test for the aforementioned code - lead software questions: "Why do you like python?", "Talk me about your projects", "What experience you have with CI?", etc...
- live coding: write a python script to parse a log file, strip, splitting on string. Use of dictionary to aggregate data. Writing a automated test for the aforementioned code - lead software questions: "Why do you like python?", "Talk me about your projects", "What experience you have with CI?", etc...
General ML and DL questions.
They asked me to pick an ML model and asked detailed questions about it
3) How do you handle missing data (scenario W.R.Y cancer data)
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