The landscape is evolving quickly. Machine Learning will transition to a commonplace part of every Software Engineer’s toolkit.
In every field we get specialized roles in the early days, replaced by the commonplace role over time. It seems like this is another case of just that.
Machine Learning Engineer as a role is a consequence of the massive hype fueling buzzwords like AI and Data Science in the enterprise. In the early days of Machine Learning, it was a very necessary role. And it commanded a nice little pay bump for many! But Machine Learning Engineer has taken on many different personalities depending on who you ask.
The purists among us say a Machine Learning Engineer is someone who takes models out of the lab and into production. They scale Machine Learning systems, turn reference implementations into production-ready software, and oftentimes cross over into Data Engineering. They’re typically strong programmers who also have some fundamental knowledge of the models they work with.
But this sounds a lot like a normal software engineer.
Ask some of the top tech companies what Machine Learning Engineer means to them and you might get 10 different answers from 10 survey participants. This should be unsurprising. This is a relatively young role and the folks posting these jobs are managers, oftentimes of many decades who don’t have the time (or will) to understand the space.
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