Fragen im Bewerbungsgespräch: Machine learning engineer | Glassdoor.de

Fragen im Vorstellungsgespräch: Machine learning engineer

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Fragen aus Vorstellungsgesprächen für machine learning engineer, von Bewerbern geteilt

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Implement a CNN classifier in Keras syntax and NO dataset was given.

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I was a bit surprised by this, as NO dataset is given and such a large task is asked over a phone. I tried to form my approach that I would first try basic classifier and they explain him about how CNN can be formed.

The task was to rank the sensors according to their importance/predictive power with respect to the class labels of the samples

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To rank the sensors according to their importance/predictive power with respect to the class labels of the samples (on Python)

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The offline tasks was rather challenging, something I found quite enjoyable. These tasks involved applied mathematics exercises and one practical data analysis task. The different interviews were interactive and open discussions. The questions weren't the typical interview questions that one get asked frequently. One of the most interesting questions that I got asked was: What is one negative aspect of you that we should be aware of if we hire you ?

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The telephone interview was reasonable was about Machine learning, Estimation theory, software engineering and C++ coding. The on-site interview was a bit biased to software engineering. There was no question about machine learning and it was solely C++11 , design patterns and white board coding. The questions like - name design patterns and show how to use one of them on the board - difference between modern c++ and the classical - what is rule of 5 etc The white board question: - given a text document write a c++ code to retrieve a text

Build a machine learning model to map the measurement of three accelerometer time series data with a time series data of an optical sensor

The first call - general HR questions about the company, my expertise and motivation. The technical interview - asked about my previous projects, how I approached various problems, theoretical ML related questions (e.g. what is the difference between decision tree and random forest)

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