Main image of article Machine Learning Engineer Career Path: What You Need to Know

When it comes to tech skills, machine learning is only getting hotter. Companies of all sizes want tech professionals who can build and manage self-learning A.I. models, and then integrate those models into all kinds of next-generation apps and services.

According to levels.fyi, which crowdsources compensation information for various tech roles, compensation for those specializing in machine learning and A.I. has increased 2.7 percent over the past six months, from an average of $246,000 per year to $252,535. Those with lots of experience and skill in machine learning can command exponentially higher salaries, of course, especially at big companies known for extremely high pay.

But what does it take to launch yourself onto a machine learning engineer career path, and once you’re there, what sort of options are available to you? Let’s dive in.

What kind of education do I need to become a machine learning engineer?

Before embarking on this career path, it’s important to have a solid foundation in computer science and math, including an understanding of how computers and algorithms work.

Programming is an essential skill, and multiple coding languages may be required, depending on the role and company. Python and JavaScript are often the most popular programming languages that aspiring machine learning engineers focus on first, followed by supporting frameworks like TensorFlow and PyTorch. 

“Once you've built that foundation and developed your core skill sets, the next step is to start applying what you’ve learned,” explains Neil Ouellette, senior machine learning engineer at Skillsoft. It’s important to gain hands-on practice and experience by experimenting with different algorithms and creating small projects on Github.

“This is a good way to not only sharpen your skills, but to build a portfolio of work that you can eventually share with prospective employers,” he explains.

Do you need formal education to become a machine learning engineer? That’s a great question. Given the demand for ML and AI engineers, many companies are willing to hire tech professionals who don’t have a formal two- or four-year degree, provided they can prove during the interview process that they have the skills necessary to succeed in the role. Before you begin applying for jobs, make sure you have a solid grasp on the following, which pop up frequently as requirements for machine learning engineer roles:

  • Researching, designing and implementing ML models and systems
  • Implementing machine learning algorithms and tools
  • Scaling data science prototypes
  • Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
  • Performing statistical analysis
  • Executing tests and optimizing machine learning models and algorithms
  • Monitoring systems in production and retraining them to improve performance
  • Utilizing machine learning libraries

In order to carry out these tasks, you’ll need to have mastered the following:

Exploring Early Roles

Mehreen Tahir, software engineer at New Relic, says that entry-level machine learning engineers are often responsible for preprocessing and cleaning data, implementing and testing different machine learning models, and possibly deploying these models.

“This involves a lot of data wrangling and debugging, but it's an essential part of the learning process,” she says. “I always recommend beginners to start working on their own projects or participate in online competitions like those on Kaggle.”

These experiences can give you invaluable insights into the practical challenges of machine learning; they’ll also help bulk out your resume and application materials when you begin applying for roles in earnest.

An entry-level machine learning engineer (often titled as a junior machine learning engineer or machine learning intern) typically fits into the data science or engineering department of an organization. Some of the typical tasks might include assisting in the development of machine learning models, with lots of collaboration with data analysts and data scientists. As with most tech jobs, a solid grasp of “soft skills” such as communication and empathy is essential for anyone who wants to make a career out of machine learning.

“You might help in building and testing models under the supervision of senior team members, gathering and cleaning data, and learning to interpret and present results,” Ouellette says. “Many organizations expect their machine learning teams to stay current with the latest techniques and methodologies, and you may be asked to help with this.”

Advancing in Your Machine Learning Engineer Career

There are many pathways for career advancement as a machine learning engineer, whether one is interested in being a manager or individual contributor.

"After gaining some years of experience and expertise, you can advance to a senior role," Ouellette says. "These engineers usually oversee project management, design systems on a larger scale, and may mentor junior engineers."

These potential roles could include a senior machine learning engineer, lead machine learning engineer or team lead, data scientist, AI specialist, machine learning architect, or research scientist. Given the popularity of machine learning, mastering its fundamentals can open an incredible number of career tracks that increasingly rely on the technology.

“In the role of team lead, you would oversee and lead a team of machine learning engineers,” Ouellette explains. “This includes making key decisions on behalf of the team and owning the whole machine learning development process.”

In companies that heavily rely on data or A.I., advancing to the executive roles of chief data officer or chief A.I. officer means one is responsible for establishing A.I. and data-related strategies at the highest level. “If you find you have a knack for handling clients and translating business problems into data problems, a move into a data science role could be a good fit,” Tahir notes.

Working Well with Management

Data scientists often do a bit of everything, from understanding the business context to data analysis to communicating results in a way that non-technical folks can understand. Soft skills matter more than ever if you’re interested in management and want to eventually run your own team.

“In this role, you'd be less hands-on with the code and more involved in strategic decisions, team management, and liaising between your team and the rest of the organization,” Tahir says. “If you're deeply interested in the theoretical side of machine learning and want to push the boundaries of what's possible, you might consider going back to school to get a PhD and become a researcher.”

It’s important to remember these pathways aren't strictly linear, and the beauty of this field is that there's a lot of flexibility to shape one's own career based on a personal interests and skills. What do you want machine learning to do for you?

Investing in Continuing Education

To ensure continuous progression in a career as a machine learning engineer, it's crucial to stay updated with the latest advancements in the field, especially as it evolves at a rapid pace. Tools, languages, and frameworks enjoy frequent iterations and updates; if you ignore them for too long, you’ll fall behind.

Maintaining your baseline knowledge involves taking online courses, attending workshops, webinars, or conferences, and regularly reading relevant research papers. “Another key is to constantly work on challenging projects, either at work or in your spare time, that push the boundaries of your current skill set,” Tahir says. “This hands-on experience is invaluable and can often expose you to new tools and techniques.”

Networking is also essential: joining professional groups, online communities, and attending industry events can help you machine learning pros connected, learn from peers, and open new opportunities.

Keeping Soft Skills Strong

From Tahir's perspective, it's also important to develop soft skills, including communication, teamwork, and problem-solving skills: “These are vital, particularly as you move into more senior or managerial roles… Demonstrating your ability to effectively communicate complex ideas to non-technical team members or stakeholders can significantly boost your career progression.”

Ouellette agrees it's critical to know how to communicate with non-technical audiences. “Although machine learning is inherently complex, you’ll often need to explain how algorithms and statistical models work with stakeholders or clients who may not have a technical background,” he says. “Strong communication skills are a must.”