“Machine Learning Engineer” is a relatively new job title, so it’s not surprising that many managers and interviewers don’t know what to ask or look for during an interview. However, if you’re looking for a qualified, competent, and hardworking ML engineer, it’s vital that you have some knowledge of the field so that you can tell if a candidate knows what they’re talking about.
The interview process is usually the part where the company chooses who the best person for the position will be. Thus, giving your best in answering an interview question from the interviewer will give them an idea on how good you will perform for the position, from handling data to the learning algorithm to be used in a certain instance.
The machine learning interview question might sound a bit different because the job requires a more technical and specific interview question to pick the right person for the job.
With that in mind, let’s take a look at some of the best interview questions you can a machine learning engineer candidate.
Here are the top 9 interview questions for a machine learning engineer.
You’ll need to know what experience they have, so you can double-check this against their resume and references. This question is important in an ML interview.
You also need to know if they already use the same tools that your business currently uses, because if not, you’ll need to estimate how long it will take to train them on new software. Unsupervised learning, supervised learning, and supervised machine learning will also come in handy.
Data pipelines are a key part of machine learning, so you’ll need to know that your candidate both has experience in writing pipelines and knows how to do so efficiently.
However, the answer you accept to this will depend on whether you’re hiring a junior, mid-level, senior, or manager, so you should know ahead of time what exceptions you’re willing to make.
The ability to keep up with new technologies and adapt to new practices is vital in machine learning. This question in a machine learning interview can help you to evaluate how much a candidate knows about future technologies, their ability to evaluate the impact of those innovations on their professional field, and their ability to creatively apply new ideas to their current knowledge.
This question is intended to assess whether the candidate can deliver feedback to a superior honestly, but also respectfully. It also gives you a deeper understanding of how much they know about machine learning and how it’s utilized in your business. It’s a good sign if your candidate is able to critically evaluate how machine learning is used in your organization and offers practical solutions without disparaging current efforts within your company.
Data sets can be unbalanced for many reasons, so a good candidate should ask for more details about how the dataset is unbalanced and if they have any information about how it ended up that way. It does not take one to be a data scientist or an expert in data science to answer this question. It’s good to have a scenario ahead of time if they ask this. This question tests their development process, how they tackle professional problems, and their decision-making.
There’s no right or wrong answer to this question as it’s intended to assess a candidate’s ability to think of the bigger picture and how their skills fit into the company. A great candidate will be able to link their skills to revenue-generating aspects like user retention, user experience, reducing data errors, or even improving efficiency with clients.
This sounds like a question straight out of a beauty pageant, but this interview question will help you with this job. A ROC curve represents the contrast between true positives and false negatives, and this is key knowledge for an ML engineer.
This question is a great way to evaluate both the candidate’s knowledge of machine learning and their ability to communicate technical information to non-technical people. This question does not require some sort of deep learning, though it would help if you know the proper words to use to explain it as humanly as possible.
Also, this is in no way a behavioral question of some sort. This question will just help the interviewer choose the best person who can explain it to someone who is new in the industry. Lastly, this will also set the person who knows the system design the best among the other candidates.
There’s no real right or wrong answer to this, but it’s a way to evaluate your candidate’s ability to communicate in a succinct manner, which is often key in a fast-paced development environment. It can also help to demonstrate your candidate’s ability to stay calm under pressure and keep focused on the task at hand.
This question evaluates a candidate’s ability to apply their machine learning knowledge to a very new technology that little is known about. A good candidate should explain how they build training datasets, as well as how they decide which data should be used to train a machine learning model. This will demonstrate their ability to come up with innovative solutions.
Knowing the best questions to ask a machine learning engineer is the hardest part of recruiting, and knowing what to look out for can make the difference between hiring a great engineer and hiring someone who will cost your company more than they generate. Asking the right questions will help you get your training set in place for the candidate’s needs once they are hired.
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