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

Written by Coursera • Updated on

Machine learning engineers work with algorithms, data, and artificial intelligence. Learn about salary potential, job outlook, and steps to becoming a machine learning engineer.

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Are you interested in becoming a machine learning engineer but not sure where to begin? While this tech job isn't an entry-level position, the 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 it receives more data. Named #1 job in 2019, machine learning engineers saw a 344 percent growth in job postings between 2015 and 2018, and we can guess that it has steadily risen since 2018 as machine learning has continued to be incorporated into all types of industries.

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 [1].

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 Facebook use machine learning to target advertisements at users based on their preferences, likes, and posts to the website. Similarly, shopping websites like Amazon uses algorithms to suggest items to buy based on a customer's purchases and viewing history [2].

Read more: Is Machine Learning Hard? A Guide to Getting Started

What machine learning engineers 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 opportunities 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 are growing at 21 percent through 2031, which is much faster than the average for all occupations [3]. The median annual salary is high at $131,490 [3]. 

Job outlook

In 2019, Indeed listed machine learning engineer as its #1 job of the year, 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 [4]. Due to changes in society as a result of the COVID-19 pandemic, the need for enhanced automation of routine tasks is at an all-time high.

Salary potential

Like many high-level technology and computer science jobs, machine learning engineers earn salaries significantly above the national average, often over six figures. In fact, as of June 2022, the average base salary for a machine learning engineer is $125,672 according to Indeed [5].

How to become a machine learning engineer

It's possible to work your way up to becoming a machine learning engineer. There are three essential steps to becoming a machine learning engineer that you'll need to take.

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. Most machine learning engineering jobs will 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 will lead to a machine learning career include: 

3. Earn an advanced degree

While it is possible to work in data science and artificial intelligence with just a bachelor's degree, pursuing a master's degree or Ph.D. 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 internships or apprenticeships.

Get started in machine learning today

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 get started today.

Build your knowledge of software development, learn various programming languages, and work towards an initial bachelor's degree. A variety of certificates and even computer science degree pathways on Coursera can help prepare you for an exciting career in the machine learning field.

The machine learning specialization from Stanford University is another great introduction to machine learning, in which you'll learn all you need to know about supervised and unsupervised learning.



Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ...


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Average time: 1 month(s)

Learn at your own pace

Skills you'll build:

Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification

Article sources


IBM. "Machine Learning, https://www.ibm.com/cloud/learn/machine-learning." Accessed November 8, 2022.

Written by Coursera • Updated on

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