Siemens Deep Learning Engineer Interview

Ths Siemens Deep Learning Interview has the following stages:

• Resume Shortlisting by the Hiring Manager
• Initial Telephonic Round
• Onsite interview - whole day event

The Hiring Manager shortlists the resumes that he finds suitable for the position depending upon several criteria, like, 

• Educational Background
• Number of years of experience
• Number of projects
• Number of course work - either school or independent online courses
• Number of papers published
• Number of patents filed
• Depending on the role

• ML Researcher - usually a Ph.D. is preferred as the candidate shows more commitment /patience/maturity to the subject.  
• ML developer can be any software engineer with working knowledge of the ML.

Initial Telephonic round:

This is going to be a general interview on ML. The basic idea here is to judge the knowledge base of the candidate and make a decision if he is worth bringing for a Face-to-Face discussion onsite.

The usual questions asked in this telephonic round can be any ML-related topics. For more questions please read my other articles here: ML questions and DL questions.

Onsite Interview:

Depending upon the role the candidate can be asked to do a project presentation or a will directly move to the other round based on the hiring team.

The first round: Project Presentation

This can be any project you have done in the field of Machine Learning/ Deep Learning that you feel is good to present to the hiring team. The audience is going to be your future team members :) So do the best you can to make them feel that you are going to be a valuable resource person.

Next Rounding - Coding a DL/ML project:

The team decides a project that you need to do given a time considerations of 1-2 hours. The project can be anything the team thinks is a must know for the candidate. You can use any tools/library that you feel is needed to get the work done.

Sometimes the team also gives a laptop without any setup done. The candidate must get the setup done and then start coding the DL project. The setup can be right from selecting the Cuda libraries to the framework - tensorflow / pytorch installation to the coding IDE of your choice.

Once the code is completed there will be a code review round. Here the candidate has to present his code and suggest possible improvements to it. 

The Next round:

If you clear code round, you will be next interviewed for the technical concepts - usually theory and mathematical proofs of the ML/DL concepts. 

Initial questions will be in the ML domain, most of the questions can be read here ML questions Then questions on Deep Learning starts.

If you are more into perception domain then it will be more into the CNN architecture and if its NLP (more into sequence models) then more in the RNN domain.

The idea is to test the basic understanding of the candidate in the ML/DL field so that he can learn as new architecture are brought in the field.

Manager Round:

This the round with the manager of the team. Usually a low technical and more behavioral round. The basic idea is to gauge the candidates profile both EQ and IQ.

HR round:

If you are found to be the suitable candidate next would be the HR round. This can be done either on the same day or sometime later on the telephone to discuss the pay package and joining dates. 

All the best for your interview.

Do let us know how your interview experience was and any questions that were interesting to be added in this list. 

As I always say Deep Learning => Keep Learning :)