Machine Learning Engineer

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?