AI and machine learning specialists are among the fastest growing jobs in the world*. Here, you’ll find the resources you need to enter the field as an entry-level machine learning engineer.
Machine learning (ML) is an in-demand field spanning various industries, from health care to finance. As a machine learning engineer, you’ll work with a team of data scientists to research and develop machine learning solutions. You’ll create, test, and optimize ML algorithms to automate workflows and improve data management. Learn more about how to prepare for an entry-level machine learning engineer role and how Coursera can help:
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Frequently asked questions (FAQ)
Machine learning skills can open the door to a wide range of careers, as more and more companies seek to harness these techniques and artificial intelligence (AI) to automate a growing range of processes. Some companies may specifically hire for machine learning engineers, but machine learning skills can also be important for data scientists, data analysts, and data engineers.
There are more specialized roles available for machine learning experts, too. Many companies in the financial industry may employ business intelligence analysts and decision scientists who can leverage machine learning skills to automate systems for delivering market insights. And companies building Internet of Things (IoT) that rely on voice recognition or other human inputs may employ natural language processing engineers or human-centered machine learning designers.
The average base pay for a machine learning engineer in the US is $127,712 as of March 2024 [1].
According to a December 2020 study by Burning Glass, demand for AI and machine learning skills is projected to grow by 71 percent 2020-2025. The same study reports a $14,175 salary premium associated with these skills [2].