Main image of article Machine Learning Engineer: Must-Ask Interview Questions

Prepping to interview a candidate for your Machine Learning Engineer position, but unsure of what questions to ask to ensure you’re finding someone who can bring that “extra something” to your company? We’ve got what you need. Use these sample questions to help you determine which candidate is more than merely skilled and experienced in the basics—someone who will bring the acumen that can help elevate your company and support its needs above and beyond the usual. Not only will that help make the interview process easier, it will also help you uncover the tech professionals who are deep-thinkers, high-performers, and all-around true standouts.

Machine Learning Engineers are experts at using data to “train” machine learning models which are used to automate processes, from image processing to medical diagnosis, and help machines to provide accurate answers. When you’re interviewing candidates for Machine Learning Engineer roles, the conversation can get technical and detail-oriented fast.

Of course, you want to draw out specifics on how the potential hire has solved applicable machine learning problems in the past. But it’s also important to determine whether the candidate understands the core concepts of machine learning. The types of Machine Learning Engineer interview questions below can help you do that, along with giving you a sense of the candidate’s depth of knowledge and experience in this rapidly emerging area of technology, which is a subset of AI:

Interviewing another position? Check out Dice’s library of interview questions.

Question: "What is the difference between bias and variance?"

Why you should ask: Machine Learning Engineers who work with machine learning need to have a firm understanding not only of coding, but also of each of the components that go into creating a successful machine learning application. You want to know if the candidate you’re evaluating understands the impact that overfitting or underfitting can have on a machine learning application – and that should come through clearly in his or her response to this question about bias and variance.

An answer you’d hear from a standout candidate: Bias comes from a model underfitting some set of data, whereas variance arises as the result of overfitting some set of data.

Question: "Which do you think is more important: Model Accuracy or model performance?"

Why you should ask: You’ll find that most candidates, when asked this question, will try to justify the need for both accuracy and performance – and give examples of ways to improve both. But in the realm of machine learning, accuracy matters more than performance because inaccurate information isn’t useful. Highly skilled Machine Learning Engineers understand that.

An answer you’d hear from a standout candidate: While both accuracy and performance are important, and subjective to the specific application you’re building, accuracy is generally more important. If your machine learning application provides inaccurate information, it doesn’t matter how quickly it does it.

Question: "How does deep learning contrast with other machine learning algorithms?"

Why you should ask: Skilled and knowledgeable Machine Learning Engineers understand deep learning conceptually. They can therefore provide an answer to this question that’s more comprehensive than this simple textbook response: “Deep learning represents a more complex or sophisticated version of machine learning.”

An answer you’d hear from a standout candidate: Deep learning is an approach to machine learning wherein the system learns the model as a neural network. If we’re addressing the algorithms specifically, it should be noted that deep learning algorithms learn meaningful features on their own, without requiring any manual feature selection.