Main image of article Machine Learning Engineer Interview Questions: What You Need to Know

Along with artificial intelligence (A.I.), machine learning is regarded as one of the most in-demand tech professions right now. In simplest terms, machine learning engineers develop algorithms and models that can adapt and “learn” from data. Organizations everywhere are using these models to improve everything from customer service to manufacturing processes.

Machine learning engineers must possess technical knowledge in a variety of key areas, including (but certainly not limited to) computer science and programming, as well as statistics, data science, deep learning, and problem solving. If that sounds like a complex job, you’re right—but the rewards are potentially vast: machine learning engineers can easily earn six- or even seven-figure salaries, depending on the company, along with benefits and perks such as equity.

What questions are asked during a machine learning engineer interview?

Different organizations have wildly different needs when it comes to machine learning engineers, which means no two job interviews are exactly alike. That can make it difficult to determine the questions a hiring manager or recruiter might ask for a machine learning job. However, we can make some good guesses about what you might face.

Introduction/Behavior Questions

These are pretty standard questions in every job interview. The hiring manager or interviewing panel wants to see why you want to work for their specific company; they also want to evaluate whether you’re a good cultural fit. These questions might include:

  • Why do you want to work here?
  • What specifically interests you about our company?
  • Which of our products do you want to work on?
  • What do you think of our approach to machine learning and A.I.?
  • Do you like working in large or small teams, or alone?
  • How could your skills support our business?

To “win” this part of the interview, it’s important to do your research beforehand. Why are you applying to this specific company? What do you find most interesting about its processes and products? The more specifics you can offer, the better your chances of impressing your interviewer.

Technical Questions

Here’s where things get a bit trickier. Depending on the company’s tech stack, modeling needs, and other factors, the interviewer could ask you a variety of questions designed to test your technical knowledge. For example:

  • How would you handle missing data in a dataset?
  • Define false positives and false negatives.
  • What’s the difference between a Type I and Type II error?
  • How would you define supervised versus unsupervised learning?
  • Walk us through how you’d build a model. How would you test it once it’s built?
  • Describe how you’d integrate a refined model into an existing product or project.
  • Tell us how deep learning differs from machine learning, and how you’d apply both in a practical context.
  • How do you decide what kind of machine learning algorithm to use, and how does the data influence your decisions?
  • Describe how you’d build a recommendation system.
  • Describe a recent machine learning project you completed. What challenges did you face, and how did you overcome them?

Fortunately, there are a number of websites that break down dozens of machine learning engineer questions and answers, including SimpliLearn, Testgorilla, Springboard, Datacamp, and more. When you’re answering the questions, also make sure you’re breaking down everything clearly and concisely; interviewers want to know you have a solid grasp of both abstract concepts and the nitty-gritty of model building.   

For the programming aspects of machine learning, you may want to consult the company’s original job posting and note the required programming languages; if you need to brush up on your language knowledge, sites like Leetcode offer sample questions (more than a few hiring managers and recruiters will simply cut-and-paste their questions from these sites).

Technical Assessments

You may face a technical assessment as part of any machine learning engineer job interview. Some companies will provide a “homework” assignment that you can do at home; these can range from solving an open-ended problem to answering highly technical questions. Others will ask you to complete an on-site test, which could take several hours and involve lots of coding. 

How can I best prepare for a machine learning engineer interview?

Oliver Sulley, director of Edge Tech Headhunters, offers some tips on how to best prepare for these kinds of interviews. First, keep in mind that not all of your interviewers will have a firm grasp of machine learning fundamentals, so you might have to explain things very simply.

“You’re going to be faced potentially by bosses who don’t necessarily know what it is that you’re doing, or don’t understand ML and have just been [told] they need to get it in the business,” Sulley said. “They’re being told by the transformation guys that they need to bring it on board.” 

When you’re rehearsing and formulating your answers to potential interview questions, make sure to come up with ways you can use your skills and background to help your potential employer advance their machine-learning strategy, especially if their strategy is still in the most nascent stages.

“What a lot of companies are looking to do is take data they’ve collected and stored, and try and get them to build some sort of model that helps them predict what they can be doing in the future,” Sulley said. “For example, how to make their stock leaner, or predicting trends that could come up over the year that would change their need for services that they offer.”

You’ll also want to emphasize your teamwork skills, as many companies are very concerned with how well you’ll integrate into the larger group. Many machine learning engineers are strong on the technical side, but they often have to interact with teams such as operations and data science; as such, they need to be able to translate technical specifics into layman’s terms and express how this data is going to benefit other areas of the company.

“Building those soft skills, and making sure people understand how you will work in a team, is just as important at this moment in time,” Sulley added.

What are the most important machine learning skills I should know? 

A lot of data engineering and machine learning roles involve working with different tech stacks, so it’s hard to nail down a hard and fast set of skills, as much depends on the company you’re interviewing with. (If you're just starting out with machine learning, here are some resources that could prove useful.)

“For example, if it’s a cloud based-role, a machine learning engineer is going to want to have experience with AWS and Azure; and for languages alone, Python and R are the most important, because that’s what we see more and more in machine learning engineering,” Sulley said. “For deployment, I’d say Docker, but it really depends on the person’s background and what they’re looking to get into.”  

Here are some other key skills:

Programming: Popular languages for machine learning and data science include Python, R, and SQL; some companies also need tech professionals skilled in C++, JavaScript, and Java. (Here is a list of free R programming courses, as well as a list of free and paid courses for learning Python, SQL, data science and other data analysis skills online.)

Mathematics, probability and statistics: If it’s been a long time since you last took a course in mathematics, probability, and statistics, an online course is a good way to refresh your knowledge.

Machine learning algorithms and frameworks: Knowledge of machine learning algorithms and frameworks is essential to the job, and it’s a list that’s constantly growing. Fortunately, there are resources online that break down some of the more popular ones.

MLOps: Machine learning operations (MLOps) is one of the core functions of machine learning engineering. Study the lessons featured on Made with ML; if you have time and bandwidth, read “Introducing MLOps: How to Scale Machine Learning in the Enterprise,” and “Practical MLOps: Operationalizing Machine Learning Models.”

What qualities make me a good machine learning engineer candidate?  

Sulley said the ideal machine learning candidate possesses a really analytical mind, as well as a passion for thinking about the world in terms of statistics: “Someone who can connect the dots and has a statistical mind, someone who has a head for numbers and who is interested in that outside of work, rather than someone who just considers it their job and what they do.”

A good machine learning engineer candidate also asks questions: you’ll want to evaluate a prospective employers culture and fit, as well as their tech stack. By the time you walk out of the interview, you should have an idea about:

  • What the company wants to build.
  • Their vision for machine learning.
  • How your career could potentially grow within the company.

“You want to figure out whether you’ll have a clear progression forward,” Sulley said. “From that, you will understand how much work they’re going to do with you. Find out what they’re really excited about, and that will help you figure out whether you’ll be a valued member of the team. It’s a really exciting space, and they should be excited by the opportunities that come with bringing you onboard.”