What a Machine Learning Engineer Does and How to Become One

Machine learning models can automate a lot, but they can’t automate themselves. Even unsupervised and self-learning algorithms need people to build them in the first place. That’s where the machine learning engineer comes in.

If you’ve been considering a job in AI, you’ve probably come across ML engineer job postings. But what exactly do these professionals do, and how can you become one? Here’s a closer look.

What Does a Machine Learning Engineer Do?

Machine learning engineers build, train, deploy and optimize machine learning models. They work alongside data scientists and other tech professionals to help businesses capitalize on this powerful technology.

If you become an ML engineer, the specifics of your day-to-day work will vary depending on your company. However, you can expect it to involve things like:

• Designing machine learning models
• Training and testing ML models
• Ensuring ML projects align with company goals
• Observing and tweaking ML models to improve their performance

The machine learning market is skyrocketing. Predictive analytics alone will be worth $34 billion by 2030 if current trends persist. That growth means many more companies will need a lot more employees who know how to create and use these technologies. As a result, ML engineers are increasingly in demand.

How Much Can You Make as an ML Engineer?

If you’re interested in becoming a machine learning engineer, you’ll want to know how much they make. According to Glassdoor, ML engineers earn a median salary of $125,162 yearly, with an additional $25,996 in bonuses.

Even lower-paid ML engineers make roughly $124,000 a year, including additional pay. At the higher end of the spectrum, you could earn as much as $186,000 annually. If you work your way up to a more managerial ML position, your annual salary could exceed $240,000.

Of course, those numbers will fluctuate with changes in demand and the economy. Even so, there’s a lot of room there to make some serious cash as a machine learning engineer. It’ll be hard work, but if you enjoy working with technology, it could also be fulfilling.

How to Become a Machine Learning Engineer

If you think this career path is a good fit for you, here’s how you can become a machine learning engineer. It’s a long road, but the payoff is worth it if you like this kind of work.

Pursue the Necessary Education

First things first, you’ll need a relevant education. While you don’t necessarily need a degree to get into the ML field, most ML engineer positions require at least a bachelor’s degree. Ideally, it should be in a related field like computer science, software engineering or data science.

If you already have a college degree, but it’s not entirely relevant, consider some further education. You can take independent AI courses to gain the necessary skills and experience without needing another four-year degree.

Some higher-level ML engineering roles require graduate degrees. If you want to be in this field long-term, pursuing this education is worthwhile but not strictly necessary. Even if a job doesn’t require a master’s, having one makes you a more impressive candidate. That said, it’ll take substantial time and money to get one, so think carefully about your goals before pursuing this route.

Get Machine Learning Experience

A machine learning engineer should also have hands-on experience with ML models. An entry-level job on an AI team will give you that experience, but you can get it without a formal ML position, too.

If you take any ML courses, you’ll likely build and train models as part of your education. Even so, it’s best to practice it on your own time, too. Join some machine learning forums for support and guidance on developing and training ML models by yourself. These independent projects look great on a resume and give you crucial firsthand experience.

If you don’t already know how to code, you should learn it. Ideally, you should be capable in a few programming languages, as you don’t know which languages future jobs will require. JavaScript is the most popular choice, but HTML, Python and SQL are also prevalent.

Consider Additional Certifications

Machine learning can be a competitive field. To better your chances of landing a top-tier ML engineering job, you should consider some professional certifications.

Professional certifications typically involve a brief training course and some tests to prove your knowledge and experience. Most positions don’t require them, but they show potential employers that you’re proficient in areas where they need talent.

Google, Amazon, Microsoft Azure and IBM all offer machine learning-related professional certifications. They aren’t free, but they’re relatively affordable compared to additional college courses, and a certificate from such a recognizable name goes a long way. You can find certifications from other organizations, too, so do some research to find one that fits your goals and budget.

Apply for Internships and Jobs

Once you’ve completed these other steps, you’re ready to start your career as a machine learning engineer. Update your resume, gather a portfolio of ML projects you’ve completed and begin searching for available positions.

Not every company posts on every job website, so use multiple to get a better picture of your options. Some of the best job search sites to use include LinkedIn, Indeed and CareerBuilder, as these use AI to match people to relevant positions.

If you don’t have any professional tech experience, you may be unable to get an ML engineer position immediately. Consider getting a lower-level AI job to gain experience in the workplace, then work your way up to an engineering role. Similarly, internships are worth a look. They may not pay much — or at all — but if you can take them on, they give you a foot in the door for future employment.

Start Your Machine Learning Engineer Career Today

Working as a machine learning engineer can be a fulfilling and profitable career. If you think this role is right for you, follow these steps to kickstart your future today.

Machine learning is complex, so the work of an ML engineer is far from easy. The path to becoming one will be similarly long and difficult. If you’re ready for that challenge, though, there’s no better time to start pursuing it than right now.