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 World Economic Forum’s Future of Jobs Report 2025 lists AI and machine learning specialists among the top three roles expected to grow most rapidly between 2025 and 2030, with a global net growth of 82 percent [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. If you’re ready to start preparing for a role in machine learning right away, consider enrolling in Microsoft's AI & MLEngineering 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].

Read more: 10 Machine Learning Algorithms to Know

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 organization 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 (BLS), information and computer science research jobs will grow 20 percent through 2034, 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 September 2025, the average base salary for a machine learning engineer is $176,188, 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 work experience through entry-level machine learning engineer jobs.

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 Professional 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

• AWS and DeepLearning.AI’s Generative AI with Large Language Models

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.

Explore our free machine learning career resources 

Discover what’s next in machine learning with Career Chat, Coursera’s weekly LinkedIn newsletter featuring trending skills, tools, and certifications. Review key machine learning concepts or hear from industry professionals by checking out our other free digital resources: 

• Bookmark for quick access: Artificial Intelligence Glossary: Learn AI Vocabulary

• Watch on YouTube: Lab Demo - Explore & Visualize Datasets - Machine Learning with Tensorflow from Google Cloud

• Hear from a pro: 6 Questions With a Google AI Research Director

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.

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