These days, it seems like everyone is looking to build a machine learning career. Across lush Ivy League campuses and active subreddits alike, students and young professionals are plotting their next moves to break into an exceedingly hot job market.
How hot? In 2019, Indeed reported that postings for machine learning engineer jobs accounted for 179 of every one million job postings in 2018, with the total number of machine learning engineer postings growing by nearly 350% between 2015 and 2018.
Increasing demand has translated into high salaries for those with key machine learning skills. When ‘machine learning (ML) engineer’ topped Indeed’s Best Jobs of 2019 list, the reported average base salary was $146,085. This growth hasn’t leveled off just yet: LinkedIn placed machine learning engineer fourth on its 2022 Jobs on the Rise list, with a salary range of $72,600-$170,000 — a big improvement on the 2020 median US earnings of $41,535.
But what does a machine learning career look like in 2022? What education do you need, and what’s your path to progress? In this article, you’ll see that in the case of machine learning, these questions are more closely linked than they might initially appear. Read on to gain a better understanding of what a machine learning engineer does, and how graduate-level degrees can unlock higher rungs on the career ladder.
What kinds of machine learning careers are out there?
If you have machine learning skills and experience, there are a variety of high-demand, lucrative careers open to you, including:
Data scientist: A data scientist ideates and executes novel approaches that turn raw data into business insights and solutions using machine learning and other data analytics.
Data engineer: A data engineer focuses on building, streamlining, and maintaining the data pipelines that are central to data science, machine learning, and other data analytics.
Software engineer: A software engineer specializing in artificial intelligence or machine learning system design focuses on building and maintaining a particular product (an app, API, or service) that brings AI capabilities to consumers and businesses alike.
That said, the primary role for those with machine learning training — and the one we will focus on here — is that of a machine learning engineer.
What does a machine learning engineer do?
The primary responsibility of a machine learning engineer is to design, develop, and ship machine learning models — and then upkeep them once they’re shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects.
Machine learning engineers can also be responsible for managing data systems and undertaking various kinds of data analysis, such as analyzing data sets from A/B tests on a machine learning system or algorithm. All this doesn’t take place in a vacuum. ML engineers are tasked with using machine learning skills to solve real-world problems, and so their efforts can have a measurable effect on the lives of real people.
For machine learning engineer jobs, however, education is a big factor in determining the exact make-up of your day-to-day responsibilities. As you’ll see in the next sections, much of the exciting work that draws people to the field in the first place only becomes realistic after graduate-level study.
Another important caveat is that, since machine learning is still a rather nascent field, there is no one standard career path as there might be for software developers. With that said, in the next sections we will lay out the different levels of machine learning education and give you an idea of the kinds of machine learning engineer jobs each degree can unlock.