7 Machine Learning Roles and How to Get Started

Key takeaways

Machine learning (ML) is a branch of artificial intelligence with various jobs in engineering, computer science, and data science, among others. 

• One of several machine learning roles is that of machine learning engineers, who earn $161,000 annually, on average [1].

• Categories of machine learning include supervised, unsupervised, and semi-supervised machine learning.

• You can qualify for a machine learning role by earning a degree in a subject such as computer science, engineering, or programming.

Learn more about machine learning roles and how to best prepare for them. Afterward, consider enrolling in the Deep Learning Specialization. In as little as three months, you’ll have the opportunity to build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks, and apply deep learning to applications. Upon completion, add this shareable credential to your resume or LinkedIn profile.

What is machine learning?

Machine learning is a branch of artificial intelligence (AI) that focuses on aiding AI in imitating how humans learn through various algorithms. ML is primarily based on mathematics and can process large quantities of data. ML uses this data to help AI become more human-like. The three categories of machine learning are:

• Supervised machine learning: This is the most common type of ML. It uses labeled data sets to train algorithms to classify data or predict outcomes. Supervised machine learning requires human intervention to create the labeled data sets it uses.

• Unsupervised machine learning: As the name suggests, unsupervised machine learning analyzes data sets independently of human interference. It uses unlabeled data sets with no defined output.

• Semi-supervised machine learning: This combines supervised and unsupervised machine learning. It starts by using labeled data sets and moves on to utilizing unlabeled data sets once the labeled data sets have been exhausted. 

7 machine learning roles

Learn more about seven machine learning roles below, including details about their average salary, job outlook, requirements, and everyday responsibilities. 

*All salary information represents the median total pay from Glassdoor as of April 2026. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Machine learning engineer

Average annual US salary: $161,000 [1]

Job outlook (projected growth from 2024 to 2034): 20 percent [2]

Requirements: To become a machine learning engineer, you must have a strong statistics and mathematics background. It is necessary to be proficient in programming languages such as C++, Python, and Java, and have a solid understanding of computer programming. 

Responsibilities: A machine learning engineer is a programmer who develops AI systems, specifically machine learning systems. As an ML engineer, you will have a wide range of responsibilities related to the system-building process, such as organizing data, performing tests, and optimizing the system. 

Data scientist

Average annual US salary: $155,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [4]

Requirements: Many data scientist employers require at least a bachelor’s degree in fields like statistics, data science, computer science, and mathematics. It is essential to have experience working with different types of data, knowledge of big data platforms like Apache Spark, Kafka, and Hadoop, and programming languages like R, Python, and SQL. 

Responsibilities: Data scientists are responsible for collecting, organizing, and analyzing data to gain valuable insights. Depending on the organization, responsibilities can also include creating data visualizations and developing statistical models. 

AI engineer

Average annual US salary: $142,000 [5]

Job outlook (projected growth from 2024 to 2034): 20 percent [2]

Requirements: Many AI engineers have bachelor’s degrees in AI-related fields, such as data science, computer science, information technology, and statistics. They also need a good understanding of mathematics and experience working with programming languages like Python, C++, Java, and R.

Responsibilities: An AI engineer is responsible for developing, programming, and training AI models. AI engineers are crucial to creating, implementing, and performing AI models. 

Software engineer

Average annual US salary: $149,000 [6]

Job outlook (projected growth from 2024 to 2034): 7 percent [7]

Requirements: A common entry-level requirement for software engineers is a bachelor’s degree in computer science, software engineering, or a related field. It is essential to have a working knowledge of programming languages like Python, C++, and Java, and at a minimum, a familiarity with Linux/Unix, Perl, Shell, and SQL.

Responsibilities: A software engineer is responsible for designing and building software solutions, which can include anything from computer games to business applications to network control systems. 

Software developer

Average annual US salary: $122,000 [8] 

Job outlook (projected growth from 2024 to 2034): 15 percent [9]

Requirements: Software developers commonly need at least a bachelor’s degree in a related field, such as computer science or engineering. They must also be able to write code and have a working knowledge of programming languages. 

Responsibilities: Software developers aim to find the correct program or code for the project they are working on. In some companies, this can include writing the code themselves. 

Business intelligence developer

Average annual US salary: $132,000 [10]

Job outlook (projected growth from 2024 to 2034): 15 percent [9]

Requirements: Business intelligence (BI) developers hold at least a bachelor’s degree in computer science or a related field.

Responsibilities: A business intelligence developer works with businesses to develop and maintain business interfaces. You'll likely work with a team that includes data scientists and data engineers, and have a keen understanding of the industry in which you work.

Computational linguist 

Average annual US salary: $131,000 [11]

Job outlook (projected growth from 2024 to 2034): 20 percent [2]

Requirements: A bachelor’s degree in computer science is not always required, but it’s beneficial when looking for a job as a computational linguist. 

Responsibilities: A computational linguist works with models that improve human language processing. This can include research, creation, and maintenance of these models. 

Machine learning career roadmap

When you embark on a career in machine learning, you may first start by completing the typical educational requirements. These may include a bachelor’s degree in data science or math. Then, you might enter the workforce in an entry-level role to build experience in the machine learning field. Eventually, you could go on to earn another degree or additional certifications that demonstrate your knowledge of machine learning. This could then help you qualify for a more advanced, higher-paying role.

How to get started in machine learning

To have a career in machine learning, learn about the various education, certification, and experience needed. 

Education

Although not always a requirement, many machine learning professionals benefit from holding a bachelor’s or master’s degree in subjects such as computer science and programming, engineering, mathematics, data science, and statistics. A degree in one of the fields mentioned above shows that you have at least a basic knowledge of the subjects necessary to perform the job you are applying for.

If you do not have a bachelor’s degree in a related field, getting an educational certificate in the role you want to land might be beneficial. For example, graduate certificates are available from various universities in computational linguistics, data science, and applied machine learning. 

Read more: Machine Learning for Education: Transforming Teaching and Learning

Certifications

Machine learning jobs will often require some knowledge of programming languages and big data platforms. Certifications can be a great asset in showcasing your professional understanding of various programming languages, such as Python, SQL, C++, and Java. 

Experience 

Many machine learning roles have entry-level options, which recent graduates commonly utilize. Because of this, experience is not always necessary to land a role, but it can strengthen your resume. Internships can also be a helpful way to show that you are capable of applying your education to a professional environment. 

Explore free machine learning resources

Discover fresh insights into your career or learn about trends in your industry by subscribing to our LinkedIn newsletter, Career Chat. Or if you want to keep learning more about AI tools and machine learning, check out these free resources:

• Watch on YouTube: Why I Waited to Learn Machine Learning (& Why I Shouldn't Have)

• Take the quiz: Machine Learning Career Quiz: Is It Right for You? Find Your Role

• Gain insights: 6 Machine Learning Certificates + How to Choose the Right One for You

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