Machine Learning Engineer Interview Questions

Find your next A-game Machine Learning Engineer with these sample interview questions. Don’t forget to add questions specific to your company’s position requirements.

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Machine Learning Engineer qualifications to look for 

Machine learning engineers focus on the design and application of models built with machine learning to solve real-world problems. 

Your top machine learning engineer has a background in both the theoretical basis and the practical applications of machine learning. Plus, they will have experience working with specialized tools and packages for machine learning such as sci-kit-learn (Python), Spark ML, R, Mahout and so on. 

Additionally, they should have a strong skill set in related fields such as statistics, optimization, data mining, and algorithmic design.

Look for candidates with a strong research background with a computer science or statistics education, and possibly a Ph.D. in a related field.

Top tip: Hire candidates willing to grow by making sure their personal career goals align with your company's mission.

Machine Learning Engineer problem-solving interview questions

Model-specific questions

Start a technical conversation by asking a candidate to describe how a familiar model works. These are some questions you can use to generate this conversation. 

  • What problem does the model attempt to solve?
  • Is it prone to over-fitting? What can be done about this?
  • Does it make any important assumptions about the data? 
  • When might these assumptions be unrealistic? 
  • How do we examine the data to test whether these assumptions are satisfied?
  • Does the model have convergence problems? 
  • Does it have a random component or will the same training data always generate the same model? 
  • What alternative models might we use for the same type of problem that this one attempts to solve? Can you compare all of these options? 
  • Can we update the model without retraining it from the beginning?
  • How fast is prediction compared to other models? How fast is training compared to other models?
  • Does the model have any meta-parameters? Will they require tuning? How do we do this?
  • How do we manage random effects in training?
  • Explain the types of data the model can handle.
  • Can the model handle missing data? What could we do if we find missing fields in our data?
  • How interpretable is the model?

Machine learning questions

  • Give an example of an application of non-negative matrix factorization
  • What is a random forest-based? What particular limitation does it try to address?
  • What methods for dimensionality reduction do you know? How do they compare with each other?
  • What are some good ways for performing feature selection that does not involve an exhaustive search?
  • How would you evaluate the quality of the clusters that are generated by a run of K-means?
  • Explain the EM algorithm. What are some applications?
  • What is deep learning?  How does it differ from traditional machine learning?
  • Explain a generalized linear model.
  • Describe a probabilistic graphical model.
  •  What is the difference between Markov networks and Bayesian networks?

Other questions

  • Explain the tools and environments you’ve used to train and assess models.
  • What research experience do you have in machine learning or a related field? Have you been published? If so, tell me about your publications. 
  • What experience do you have with Spark ML or another platform for building machine learning models using very large datasets?

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