Machine learning (ML) is as much a buzzword as it is a profession. Still in its early days, it’s both captured our imagination and scared us silly at the same time. Our data shows jobs in this realm are not going away; in fact, the machine learning job market is amongst the most steady we’ve seen.
Machine learning is a discipline that pops up routinely, despite being relatively nascent. Apple is investing in ML with its own Swift-ready apps and services, for example. Both CoreML and Create ML focus on Swift, which both bolsters machine learning as a useful discipline and shows it’s ready for use with any language. Other companies are also bringing more tools online: Google is also investing in machine learning with ML Kit, though that’s more about Firebase than Android (or even Kotlin).
The rise of ML isn’t too shocking. In early 2018, GitHub said it would play a big role in tech for that year and beyond. Our jobs data proves that point; not only was 2018 a banner year for machine learning jobs, it was a continuation of a robust trend.
(If you’re wondering why jobs spike at the beginning of each year, this is likely a time for contract renewal for many firms. Many jobs in tech operate on one-year contracts that span the calendar year. Many contract jobs are picked back up by the contractor at the start of the new year, but employers do post the position as a safeguard in the event their contractor decides to move on. While these spikes show there’s a “good” time to look for machine learning jobs, the overall trend is important to watch.)
Excluding the aforementioned spike at the start of each year, we see machine learning jobs on an almost perfect upward trajectory. We like this for many reasons; it’s a job market with positive, pragmatic momentum. While “hockey-stick” moments in job markets are exciting and interesting (we recently examined this for Kotlin), a staid upward trajectory shows solid foundational support.
There is a lot going on in tech that favors ML, too. Data science, one of the top skills for machine learning jobs, is just now solidifying Python as its language de rigueur, eschewing the R language. In the R-versus-Python comparison, not only is the latter more scalable, it’s more popular, and has use cases beyond machine learning.
A recent Axios article shows major tech companies such as Apple, Google, Microsoft, Facebook, and Amazon are making frequent acquisitions of artificial intelligence (A.I.) firms. This gobbling of smaller companies only helps to feed the machine learning jobs pipeline at the lower end of the spectrum. There’s no sign these companies will slow or stop their eagerness for artificial intelligence and ML anytime soon.
At scale, other job types still beat machine learning. There are, for instance, more ‘iOS Developer’ or ‘Python Developer’ jobs on Dice than there are machine learning positions. But machine learning is also surmounting growth hurdles, and has proven itself useful for many industries and segments. Machine learning might seem relatively new to many of us, but the data shows it’s here to stay.