ML Engineer

Machine Learning Engineers are technically proficient programmers who research, build, and design self-running software to automate predictive models. An ML Engineer builds artificial intelligence (AI) systems that leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions.

Each time the software performs an operation, it “learns” from those results to carry out future operations more accurately.

Designing machine learning systems requires that the Machine Learning Engineer assess, analyze, and organize data, execute tests, and optimize the learning process to help develop high-performance machine learning models.

What Does a Machine Learning Engineer Do?

Machine Learning Engineers are highly skilled programmers who develop artificial intelligence (AI) systems that use large data sets to research, develop, and generate algorithms that can learn and make predictions.

Overall, this role is responsible for designing machine learning systems, which involves assessing and organizing data, executing tests and experiments, and generally monitoring and optimizing machine learning processes to help develop strong performing machine learning systems.

Where Do Machine Learning Engineers Come From?

Although you’ll find a Machine Learning Engineer can start in any number of disciplines, most ML Engineers have a background in computer science, engineering, mathematics, or data science.

A study from Indeed highlighted the differences in backgrounds for Machine Learning Engineers and other related roles, like Data Scientist, Software Engineer, Data Analyst, and Data Engineer.

Indeed’s numbers showed that the Data Scientist role clearly has the most diverse fields-of-study of these related job titles we’ve looked at, while the Software Engineer role attracted the least diverse educational backgrounds. In the case of the Machine Learning Engineer role, meanwhile, more than 60 percent of Machine Learning Engineers come from a computer science or engineering background, and they’re almost twice as likely to be from these backgrounds than someone holding the title “Data Scientist.”

As far as their professional backgrounds, the study found that the most likely prior job title for a Machine Learning Engineer would be “Software Engineer.” Many other ML Engineers are work in academia before turning to a career in machine learning.

But it’s important to remember that data science and machine learning are still in their relative infancy as fields of study and as many companies in tech and beyond are looking to build out their data science teams, new pathways to a Machine Learning Engineer are becoming possible.

Although you do need a solid foundation in math and computer science, many are picking up the other skills and knowledge areas necessary to become a Machine Learning Engineer – for example, understanding supervised and unsupervised learning, deep learning, regression, classification, clustering methods, and neural networks – by pursuing a certification course, many of which can be completed online.

Characteristics of a Successful Machine Learning Engineer

Every great Machine Learning expert would seem to have a few traits in common. Here are the characteristics of a successful Machine Learning Engineer:

They’re Solid Computer Programmers

If you’re looking to pursue a career in AI and machine learning, you’ll need to learn to program. A programmer should understand frequently used languages including C++, Java, and Python, and it doesn’t end there. Languages like R, Lisp, and Prolog have also become important languages for machine learning. Still, not all successful machine learning engineers need to necessarily be experts in HTML or JavaScript.

They Love the Iterative Process

Machine learning is by its nature an iterative process. To be effective in this role, one needs to actually enjoy that style of development. Building a machine learning system means one builds a very simple model quickly, to begin with, then iterates on getting it better with each stage.

Again, though, a good Machine Learning Engineer can’t be too stubborn. You need to develop an understanding of when it’s time to stop. It’s always possible to improve the accuracy of any machine learning system by continuing to iterate on it, but one needs to learn to develop an intuition for when it’s no longer worth the time and effort.