The top 10 Machine Learning Engineer Interview Questions

I had very intense job interview processes for the Machine Learning Engineer (MLE) and Data Scientists positions. In this post, I am going to highlight the most frequently asked and important questions in MLE interviews.

In general, MLE interviews consist of 4 different interviews including talking about past experiences and motivation for the role, behavioral questions, technical questions (statistics and ml), and coding. The last interview can be a take-home. We can divide technical and coding questions into 3 sections as follows: Statistics&Math, Machine Learning, and Coding&Code Review.

Statistics&Math

1. If you need to remember Hypothesis Testing, do it asap :) Because you should definitely expect a question as follows: Can you explain what hypothesis testing is? Keep mentioning what is a null hypothesis. Then the next possible question is; Do you know what is the p-value and how is it used in hypothesis testing? It would be nice and clear by giving an example to support your answers.

2. Normal distribution is very popular in MLE interviews. You should check the job description for other relevant distributions. What is Normal distribution? Why it is important, especially for statistical tests? What is the relation between the Normal distribution and the Central Limit theorem?

Machine Learning

3. “Can you explain the pipeline of MLE?” You should show your knowledge about the overall pipeline of MLE. The possible follow-up question would be CI/CD pipeline and production. This is one of the biggest differences between MLE interviews and Data Scientist interviews. For MLE interviews, there is an expectation to have experience with CI/CD and production.

4. Not always but it often starts with the types of ML problems. What classification and regression are? What is the difference between them?

5. During the interview, they share an ML problem and test how you are approaching it. You should propose an ML algorithm that fits a given problem well. Sometimes there is not only one unique answer. It is good to explain your approach, why do you prefer to use Algorithm A? Related to the question, you should be well prepared for questions about the model evaluation. Assume that you find the best ML model for the given problem, how do you evaluate the performance of the ML model/algorithm? What kind of metrics do you use? Why? The evaluation metric can be changed depending on your problem. So, it is important to get a good familiarity with the different evaluation metrics (e.g, Accuracy, Precision, Recall, F1-score, AUC, ROC curve, RMSE, etc.) used in ML problems.

6. Can you explain what overfitting and underfitting mean in the context of Machine Learning, and how can they be avoided? Can you explain the bias-variance tradeoff? When an ML model can overfit or underfit in terms of bias and variance?

7. Regularization can have a big impact on ML models. Another important question can be; what is Regularization? When and how to use it?

8. Testing is important for MLE. It is good to know how to write tests for ML models.

Coding & Code Review

Python is the most commonly used programming language for MLEs. I had coding interviews in Python. For MLE coding interviews, you should have practiced Python apply, lambda, groupby arguments, and ML-related libraries such as pandas, numpy, and matplotlib.

9. You can have a live or take-home coding session. If you have a live coding session, one of the key points is that you should think loudly. Assume that the other person(s) is a colleague of yours. You explain the problem, what is your approach to solving this problem, and tell the implementation when you complete it. Then they will also ask you about the complexity. If you don’t have enough coding practice, you can review notebooks for solutions to ML problems from Kaggle and also solve questions from platforms such as Project Euler, LeetCode, and so on.

10. During your coding technical interview, you can have a Code Review session. They will show you a piece of code and ask questions as follows: Can you explain what this given function does? If you review this code, do you have any advice/comments for improving this code in terms of time and space complexity? It helps you know which built-in functions you prefer to use and when, and also you are familiar with coding best practices.

Thanks for reading. I would like to know each of them before my job application. Hope this post helps you. If you have different questions that you find important, please leave a comment :)

P.S. Keep in mind that questions asked during an interview can vary depending on the company and the specific role. The questions here are generated from my MLE interview experiences for junior and mid-level positions.