Fragen aus Vorstellungsgesprächen für Machine learning engineer, von Bewerbern geteilt
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)
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 ?
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
- Why did you choose your field of study? - How did you decide on the topic of your thesis? - How can the problem you are trying to solve in your thesis, be applied to Siemens' business? What problems can be addressed in a similar fashion?