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Machine Learning Engineer

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.

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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