10 questions machine learning engineers can expect in a job interview

Demand for machine learning engineers has exploded in the past two years, as AI development and adoption continue to grow across industries, according to a report from Indeed. These professionals are among the most in-demand tech professionals, and among the highest paid, with average salaries of $134,449 in the US, according to another Indeed report.

“Software is eating the world and machine learning is eating software,” said Vitaly Gordon, vice president of data science and software engineering for Salesforce Einstein. “Machine learning engineering is a discipline that requires production grade coding, PhD level machine learning and a business acumen of a product manager. Finding such rare people can uplift a company from a follower into a leader in their space, and everyone is looking for them.”

Those applying for machine learning jobs can expect a number of different types of questions during an interview, said Colin Shaw, director of machine learning at RevUnit. “Good machine learning engineers have a blend of a variety of skills and also know how to fuse this knowledge into code that can be taken to production,” Shaw said. “The general areas of interest that we look for include mathematics and statistics, machine learning and data science, deep learning, general knowledge and problem solving, and computer science and programming.”

It’s also important for the job applicant to arrive at the interview with questions of their own for the hiring manager, said Dave Castillo, managing vice president of machine learning at Capital One.

“An interview is a two-way conversation,” Castillo said. “Just as important as the questions that we ask are the questions that candidates ask us. We want to ensure that not only is the candidate the right choice for the company, but the company is the right choice for the candidate.”

Here are 10 questions machine learning engineers may be asked during a job interview.

This question can act as an entry point to the interview, said Zachary Hanif, director and principal machine learning engineer at Capital One. Hiring managers can follow up with more detailed questions like, “What were the most surprising business and technical difficulties you encountered through this project?” and “During the project, what, specifically, was your day-to-day role?”

“After asking these, we have a good idea of what the candidate’s background is, and the nature of their interests,” Hanif said. “It’s here that we tend to ask deeper technical questions about their experience.”

Simple questions like this allow hiring managers to test major statistical concepts, said Petr Tsatsin, head of engineering at Ople. “Linear regression encompasses many statistical concepts that are the foundations of machine learning,” Tsatsin said. “From linear regression, the interview can dive deeper and lead to testing concepts on different types of distribution, over-fitting, and many more.