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Machine Learning Engineer Interviews

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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.

8,203 machine learning engineer interview questions shared by candidates

During the historical data collection period (of some given time series data about wind turbine bearing temperature), several wind turbines had generator bearing problems. Four wind turbines had generator bearing failures and replacements. The symptom of bearing fault is rising temperature beyond normal range. The task is to build the ML model to detect anomaly in generator bearing and identify wind turbines that shows generator bearing defect. You are required to submit the following: List of WT’s that are suspected to have a generator bearing defect during the data period including 4 that had change out, Result showing the reason for diagnosis, and the code associated with the aforementioned. You are then given ids for wind turbines that had NO generator bearing defect (healthy).
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Machine Learning Engineer

Interviewed at ONYX InSight

3.9★
Mar 26, 2019

During the historical data collection period (of some given time series data about wind turbine bearing temperature), several wind turbines had generator bearing problems. Four wind turbines had generator bearing failures and replacements. The symptom of bearing fault is rising temperature beyond normal range. The task is to build the ML model to detect anomaly in generator bearing and identify wind turbines that shows generator bearing defect. You are required to submit the following: List of WT’s that are suspected to have a generator bearing defect during the data period including 4 that had change out, Result showing the reason for diagnosis, and the code associated with the aforementioned. You are then given ids for wind turbines that had NO generator bearing defect (healthy).

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