The field of Machine Learning is growing rapidly, which means big things for both engineers and companies looking to hire a machine learning engineer. Not convinced? Just take a quick look at our Stack Overflow Trends tool, and you’ll see that interest in the term has grown by 1380% since 2009.
The work that Machine Learning Engineers do is not only interesting, but also incredibly complex, so it’s no surprise that companies may have a hard time finding one. The field itself is highly technical and requires a heady mixture of systems design, math, stats, engineering, and domain knowledge. Machine Learning Engineer roles can vary greatly depending on the company and their needs – they could be developing new models, applying existing models to new domains, or applying models in known successful ways.
Here are a few tips on what Machine Learning Engineers look for when applying to new jobs, and how this can impact your hiring efforts.
It’s hard to find a field that couldn’t benefit from advances in machine learning. Products such as speech recognition, Internet fraud detection, genome analysis, and artificial intelligence, are all things that Machine Learning Engineers work on. Matic Horvat, Head of Data Science at Cytora, talks about how important this is to him, saying “[The company I work for now] is reimaging the way risk is quantified and priced in the insurance industry. In my role, I get to play a vital part in achieving that, and I find that very rewarding.”
Machine learning has gone from something only huge software companies work on to becoming something any company can use to tackle practical problems within their industry. But that doesn’t mean that just any company will do – Machine Learning Engineers want to work for a company that not only understands the concept but also values it.
Ben Lengerich, a Machine Learning Intern, says, “Team leaders must both understand machine learning (to know what is possible) and have domain knowledge to identify impactful problems. Additionally, the company must have a strong engineering team to support machine learning.”
The one piece of advice that we heard over and over was that companies should really do their research to determine what type of role they’re looking for. Lots of companies think they need one type of developer, but then after going through a few interviews, they realize they should be looking for something else.
Peadar Coyle, a Machine Learning Engineer based out of London, says, “I think it's really important for recruiters and companies to be aware of what kind of specialist they want. Do they in fact want a data engineer (someone working on building scalable systems using technologies like Spark), or a machine learning scientist (someone working on research problems - with a long time horizon)? There's a lot of people with the same job title (like Data Scientist), but with very different skills.”