7 Machine Learning Roles and How to Get Started

The increased use of artificial intelligence has given life to new technologies and new careers within those technologies. Machine learning (ML) is a branch of artificial intelligence that has sprouted various jobs in engineering, computer science, and data science, among others. Learn more about some of the jobs machine learning has to offer and how to best prepare for them. 

What is machine learning?

Machine learning is a branch of artificial intelligence (AI) that focuses on aiding AI in imitating the way 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 interference to create the labeled data sets it uses.

• Unsupervised machine learning: As the name suggests, unsupervised machine learning analyzes data sets independent 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 common responsibilities. 

Machine learning engineer

Average annual US salary (Glassdoor): $122,637 [1] Job outlook (projected growth from 2023 to 2033): 26 percent [2]

Requirements: To become a machine learning engineer, you will need to have a strong background in statistics and mathematics. 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 (Glassdoor): $117,869 [3]

Job outlook (projected growth from 2023 to 2033): 36 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 important to have experience working with different types of data, and the knowledge of big data platforms like Spark, Kafka, and Hadoop, and programming languages like R, Python, and SQL. 

Responsibilities: Data scientists are responsible for collecting organizing data, and analyzing that 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 (Glassdoor): $133,816 [5]

Job outlook (projected growth from 2023 to 2033): 26 percent [2]

Requirements: Many AI engineers have bachelor’s degrees in fields related to AI, 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 (Glassdoor): $118,107 [6]

Job outlook (projected growth from 2023 to 2033): 7 percent [7]

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

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

Software developer

Average annual US salary (Glassdoor): $103,990 [8] 

Job outlook (projected growth from 2023 to 2033): 17 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 (Glassdoor): $99,613 [10]

Job outlook (projected growth from 2023 to 2033): 17 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 (Glassdoor): $97,154[11]

Job outlook (projected growth from 2023 to 2033): 26 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 the processing of human language. This can include research, creation, and maintenance of these models. 

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 aforementioned fields 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 fields like computational linguistics, data science, and applied machine learning. 

Certifications

Many machine learning jobs will 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 are commonly utilized by fresh graduates. Because of this, experience is not always necessary to land a role, but 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. 

Prepare for a machine learning role on Coursera

Various careers are available in machine learning, many of which you can prepare for on Coursera. Stand out to future employers and show your expertise by getting a certificate in Machine Learning. This program covers supervised and unsupervised machine learning, deep learning, and using ML in data analysis. Upon completion, you will gain exclusive access to career resources like resume review, interview prep, and career support.