What Is a Machine Learning Engineer? (+ How to Get Started)

Are you interested in becoming a machine learning engineer but unsure where to begin? While this role isn't an entry-level tech job, the career path to becoming a machine learning engineer can be an exciting and rewarding one.

Machine learning is a fascinating branch of artificial intelligence that involves predicting and adapting outcomes as more data is received. The demand for machine learning professionals has also grown exponentially in recent years. Recognized as the fifth most in-demand job of 2023, machine learning engineers have become highly sought-after by employers [1].

In this article, you'll learn more about machine learning engineers, including what they do, how much they earn, and how to become one. Afterward, if you're interested in pursuing this impactful career path, you might consider enrolling in IBM's AI Engineering Professional Certificate. Throughout the program, you'll build a portfolio of projects to show your expertise.

What is machine learning?

Machine learning is a part of the computer science field specifically concerned with artificial intelligence. It uses algorithms to interpret data in a way that replicates how humans learn. The goal is for the machine to improve its learning accuracy and provide data based on that learning to the user [2].

Machine learning includes everything from video surveillance to facial recognition on your smartphone. However, customer-facing businesses also use it to understand consumers' patterns and preferences and design direct marketing or ad campaigns. 

Social media platforms like Meta use machine learning to target advertisements at users based on their preferences, likes, and posts to the website. Similarly, shopping websites like Amazon use algorithms to suggest items to buy based on a customer's purchases and viewing history [3].

What does a machine learning engineer do?

Machine learning engineers act as critical members of the data science team. Their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing artificial intelligence systems. 

Often, a machine learning engineer will also serve as a critical communicator between other data science team members, working directly with the data scientists who develop the models for building AI systems and the people who construct and run them.

While job responsibilities for machine learning engineers will differ, they often include:

• Implementing machine learning algorithms

• Running AI systems experiments and tests

• Designing and developing machine learning systems

• Performing statistical analyses 

Job outlook for machine learning engineers

Over the past few decades, the computer science field has continued to grow. According to the US Bureau of Labor Statistics, information and computer science research jobs will grow 23 percent through 2032, which is much faster than the average for all occupations [4].

Indeed ranks machine learning engineer in the top 10 jobs of 2023, based on the growth in the number of postings for jobs related to the machine learning and artificial intelligence field over the previous three years [5]. Due to changes in society because of the COVID-19 pandemic, the need for enhanced automation of routine tasks is at an all-time high.

Machine learning engineer salary

Machine learning professions are typically lucrative careers, earning high salaries depending on their experience and location. Like many high-level technology and computer science jobs, machine learning engineers earn salaries in the six figures. In fact, as of March 2024, the average base salary for a machine learning engineer is $162,740, according to Indeed [6].

How to become a machine learning engineer

It's possible to obtain a career in machine learning through several paths discussed below. First, let's examine the three essential steps you'll need to take to become a machine learning engineer.

1. Earn a bachelor's degree in computer science or a related field.

Because machine learning is part of the computer science field, a strong background in computer programming, data science, and mathematics is essential for success. Many machine learning engineering jobs require a bachelor's degree at a minimum, so beginning a course of study in computer science or a closely related field such as statistics is a good first step.

2. Gain entry-level work experience.

Once you have earned a computer science degree, the next step is to start working in the data science field to gain experience working with machine learning or artificial intelligence. Some entry-level positions that can lead to a machine learning career include: 

• Computer engineer

• Data scientist

• Software developer

• Software engineer

3. Build your machine learning expertise.

While working in a related role, you can build specialized knowledge and strengthen your skill set. Consider enrolling in relevant machine learning programs and certificates to keep expanding. Here are a few recommendations to get started:

• Stanford and DeepLearning.AI's Machine Learning Specialization

• IBM Machine Learning Professional Certificate

• IBM AI Engineering Professional Certificate

• Johns Hopkins Applied Machine Learning Specialization

Do you need an advanced degree to be a machine learning engineer?

While it is possible to work in data science and artificial intelligence with a bachelor's degree, pursuing a master's degree in computer science, data science, or software engineering can help you learn the more complex tasks required of machine learning engineers. It will also give you leverage as you apply for jobs, especially if you have bolstered your studies with plenty of industry experience, such as internships or apprenticeships.

Learn about machine learning on Coursera

Artificial intelligence and machine learning are growing branches of computer and data science. Becoming a machine learning engineer requires years of experience and education, but you can start today.

• Build foundational knowledge of machine learning with the beginner-friendly Machine Learning Specialization from Stanford and DeepLearning.AI.

• Strengthen your understanding of applied machine learning techniques with the Johns Hopkins Applied Machine Learning Specialization for intermediate learners.

• Advance your knowledge and build your career as an AI Engineer with the IBM AI Engineering Professional Certificate.