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

Machine learning engineers are responsible for building artificial intelligence systems. This fascinating branch of artificial intelligence involves creating models trained on data sets that can predict and adapt to outcomes. The demand for machine learning professionals has grown exponentially in recent years, with the World Economic Forum (WEF) predicting job openings will grow by 40% by 2027 [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 Microsoft's AI & Machine Learning Engineering Professional Certificate. Throughout the program, you'll build, deploy, and innovate with advanced machine-learning techniques and real-world projects.

What is machine learning?

Machine learning is a subset of computer science and artificial intelligence that uses algorithms to learn from data in a manner not dissimilar to how humans learn. The goal is for the machine to successively improve its learning accuracy as it's trained on data sets, which slowly teach the algorithm to perform a specific task [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 are critical members of the data science team. Their tasks involve researching, building, and designing machine learning systems for artificial intelligence and maintaining and improving existing 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 from one organizations to another, 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 26 percent through 2033, which is much faster than the average for all occupations [4].

Indeed ranked 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 the increasing capability of AI systems, the demand 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 January 2025, the average base salary for a machine learning engineer is $162,297, 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.

Build your machine learning skills on Coursera

Artificial intelligence and machine learning are growing fields. Build the skills you need to enter this in-demand career with one of the following courses on Coursera:

To develop practical machine learning skills, try DeepLearning.AI and Stanford's Machine Learning Specialization. In this beginner-friendly program, you'll learn to build ML models, apply best practices for ML development, and even build and train your own neural network to perform multi-class classification.

To prepare for a career as a machine learning engineer, enroll in the Microsoft AI & ML Engineering Professional Certificate. In this intermediate-level program, you'll design and implement AI and ML infrastructure, master machine learning algorithms and techniques, and create your own AI-powered agent.

For a deep dive into AI engineering, take IBM's AI Engineering Professional Certificate. In as little as three months, you'll learn the fundamentals of AI, deploy machine learning algorithms, and build deep learning models and neural networks.