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Along with artificial intelligence (A.I.), machine learning is regarded as one of the most in-demand areas for tech employment at the moment. Machine learning engineers develop algorithms and models that can adapt and learn from data. As a result, those who thrive in this discipline are generally skilled not only in computer science and programming, but also statistics, data science, deep learning, and problem solving.

According to Burning Glass, which collects and analyzes millions of job postings from across the country, the prospects for machine learning as an employer-desirable skill are quite good, with jobs projected to rise 36.5 percent over the next decade. Moreover, even those with relatively little machine-learning experience can pull down quite a solid median salary:

Dice Insights spoke with Oliver Sulley, director of Edge Tech Headhunters, to figure out how you should prepare, what youll be asked during an interviewand what you should say to grab the gig.

Youre going to be faced potentially by bosses who dont necessarily know what it is that youre doing, or dont understand ML and have just been [told] they need to get it in the business, Sulley said. Theyre being told by the transformation guys that they need to bring it on board.

As he explained, that means one of the key challenges facing machine learning engineers is determining what technology would be most beneficial to the employer, and being able to work as a cohesive team that may have been put together on very short notice.

What a lot of companies are looking to do is take data theyve 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 they year that would change their need for services that they offer.

Sulley notes that machine learning engineers are in rarified air at themomentits a high-demand position, and lots of companies are eager to show theyve brought machine learning specialists onboard.

If theyre confident on their skills, then a lot of the time they have to make sure the role is right for them, Sulley said. Its more about the soft skills that are going to be important.

Many machine learning engineers are strong on the technical side, but they often have to interact with teams such as operations; as such, they need to be able to translate technical specifics into laymans 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.

There are quite a few different roles for machine learning engineers, and so its likely that all these questions could come upbut it will depend on the position. We find questions with more practical experience are more common, and therefore will ask questions related to past work and the individual contributions engineers have made, Sulley said.

For example:

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

For example, if its 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 thats what we see more and more in machine learning engineering, Sulley said. For deployment, Id say Docker, but it really depends on the persons background and what theyre looking to get into.

Sulley said ideal machine learning candidates posses 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, he said.

As you can see from the following Burning Glass data, quite a few jobs now ask for machine-learning skills; if not essential, theyre often a nice to have for many employers that are thinking ahead.

Sulley suggests the questions you ask should be all about the technologyits about understanding what the companies are looking to build, what their vision is (and your potential contribution to it), and looking to see where your career will grow within that company.

You want to figure out whether youll have a clear progression forward, he said. From that, you will understand how much work theyre going to do with you. Find out what theyre really excited about, and that will help you figure out whether youll be a valued member of the team. Its a really exciting space, and they should be excited by the opportunities that come with bringing you onboard.

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Machine Learning Engineer Interview Questions: What You Need to Know - Dice Insights