A machine learning engineer is critical for engineering and data analytics teams who want to build a robust artificial intelligence (AI) infrastructure. With the right machine learning engineer onboard, they can build and design AI that automates tasks, reduces human mistakes, and makes complex decisions quickly.
Hiring an experienced, capable machine learning engineer relies on asking the right interview questions.
In this article, you’ll find 36 interview questions to help you hire a machine learning engineer. Along with general questions for a machine learning engineer, you’ll also find questions for related competencies, including attention to detail, strategic thinking, and decision-making.
Read on to uncover all of the interview questions to ask a machine learning engineer.
General Interview Questions for a Machine Learning Engineer
- What got you into machine learning?
- Tell me about a modeling project you’ve worked on. What was your role, and what was the outcome?
- Tell me about a time when you took a project from analysis to an actual module on a pipeline. What was the review process like? What did you have to prove to make it part of the production environment?
- What is the structure of your team like? What are you responsible for?
- Discuss how you think about the tradeoff between bias and variance.
- What is cross-validation, and how have you recently used it in practice?
- Describe a time when you used classification instead of regression and why.
- What’s your approach to addressing missing or corrupted data in a dataset?
- What have you learned in your experience with algorithms and the types of mistakes they can make?
- Walk me through your process for building a data pipeline.
Attention to Detail Interview Questions
It’s up to a machine learning engineer to develop code, navigate complex processes and systems, and keep thorough documentation. To do all of these things well, they must have a keen eye for every detail, large or small.
The below questions can help you find candidates who are excellent at attention to detail.
- Tell me about a time when you avoided making a mistake on a project because of your attention to detail.
- Share an example of a time when you should have paid closer attention to the details of a project you were working on. What happened?
- Describe a situation in which you had the option of either delegating the details to other team members or handling them yourself.
- Give me an example of a situation where you paid too much attention to minor details and lost sight of the big picture.
- Describe a time when you chose to focus on the big picture when you should have focused more on the details.
- Do you gravitate more towards big-picture work or the details? Give me an example of a situation that exemplifies your preference.
- Describe a time when you caught a mistake that your colleagues hadn’t noticed.
- Describe a situation where attention to detail was either critical or unimportant for the task you were working on.
- Give me an example of a time when you found working with the details to be especially challenging.
Strategic Thinking Interview Questions
Strategic thinking is of utmost importance for machine learning engineers. Great machine learning engineers carefully consider every system and model to ensure they support the company’s overall strategy and goals.
The below questions can help you find candidates who are adept at strategic thinking.
- Tell me about a time when you had to understand the big picture while also managing a lot of smaller details. How did you avoid losing sight of the overall strategy?
- Imagine you have to explain a major project you’re working on to the CEO, but you only have 3-4 sentences to explain it. What would you say?
- Tell me about a time when you had to build a strategic plan. What steps did you take, and what was the outcome?
- Tell me about a time when you took a step back to better understand the big picture in a situation. How did you know you needed to take a step back, and what did you learn from the experience?
- Tell me about a time when you measured a strategy’s effectiveness. What was your process for measuring success?
- Tell me about a project you’ve worked on that connects to the larger context of your business and the market it operates in.
Decision-Making Interview Questions
A machine learning engineer is always up against important decisions that can impact the entire organization. From deciding the best data to rely on to determining the right systems and processes to build, a great machine learning engineer knows how to make well-informed decisions.
The below questions can help you find candidates who have a knack for decision-making.
- Tell me about a time when you had to make a tough decision. Why was it difficult? What was the outcome?
- Tell me about a time when you misjudged a situation and made the wrong decision. What happened, and what did you learn from the experience?
- Tell me about a time when you had to make a big decision on short notice. Walk me through your decision-making process and the outcome.
- If you could go back and change a major decision you made at work, would you? If yes, what would you change, and why would you change it?
- Tell me about a time when you had to weigh the risks and rewards of a decision carefully. What was your thought process, and what did you decide?
- Let’s say you were asked to make a major decision at work, and you could only consult one person. Who would you consult and why?
- When you have to make a big, difficult decision, what is your typical approach? For example, do you ask for advice or go with your gut?
- Tell me about a time when you fully trusted your own decision-making abilities. What made you feel confident about your decision?
- Tell me about a time when you had to make a decision with limited information. What information did you have available, and how did it help lead you to your conclusion?
- Tell me about a time when you had to delay making a decision due to a lack of information. What additional information did you need in order to make the decision?
- Describe a time when you had to make a highly technical decision. What steps did you take?