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
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?
- 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?