4 Careers in Designing Machine Learning Systems

Written by Coursera Staff • Updated on

Careers in designing learning systems are great options for people interested in working with machine learning systems. Learn about machine learning systems careers with our comprehensive guide.

[Featured image] A machine learning engineer designs machine learning systems at his desk.

Careers in designing machine learning systems are a great opportunity for people who want to work with cutting-edge technology and contribute to a business’s overall growth. This field is increasingly in demand, providing many chances for you to find a role that fits your interests and intended career trajectory. Read more about potential career paths within machine learning system design with our comprehensive guide.

Read more: Machine Learning Salary: A Guide

Entry-level jobs in designing machine learning systems

If you’re interested in growing your career within the machine learning industry, you’ll first want to find an entry-level role to pursue. These roles might include working as a data analyst or pursuing junior roles within machine learning and data science. Like other information technology roles, you’ll need to develop your computer and programming skills to ensure you’re competitive and prepared for the field. A bachelor’s degree in data or computer science is also ideal.

Computer science skills, foundational math knowledge, and data modeling experience are all important things to develop before pursuing your first role in designing machine learning systems. Some people choose to pursue advanced degrees to further specialize, but whether you need to depends on the exact qualifications of the types of roles you’re considering.

Once you’ve developed the skills needed to compete in the machine-learning industry, it’s time to apply for roles. Internships, networking opportunities, and online classes are all opportunities to learn more about potential roles. An entry-level position allows you to gain the experience necessary to advance into more specialized job titles.

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

4 career paths in designing machine learning systems

Like any career path, as you develop your skills and gain experience in the machine learning world, you’ll find opportunities to grow and progress along several different career paths. We’ve collected some examples to help demonstrate how a potential career in designing machine learning might progress.

1. Data scientist

Average annual base salary (US): $129,597 [1]

Data analysts are professionals responsible for using data to help businesses solve problems and improve processes to help reach stated metrics and goals. They work with a wide variety of data collected from various sources to discover new insights and help inform strategic planning. Data scientists work in industries ranging from public health to medical research or software design.

A typical day as a data scientist includes analyzing large data sets using machine learning and algorithms. The insights drawn from the analysis are translated into graphics, charts, or reports for executives and other decision-makers.

Data scientists usually need a bachelor’s degree in computer science, mathematics, statistics, or another subject. Many jobs offer specific training to further develop the needed skills and knowledge to find success in that particular industry. Other key skills to consider learning include computer programming languages, attention to detail, logical thinking, and analytical skills. 

2. Machine learning engineer

Average annual base salary (US): $126,293 [2]

Machine learning engineers design, build, and maintain automated machine learning systems. They input large data sets into the machine learning framework and then share those results with other data professionals, such as data scientists. The algorithms created by machine learning engineers are used across various industries, including self-driving cars, social media, and online retail. Machine learning engineers create frameworks that can function independently of human supervision or intervention.

A typical day as a machine learning engineer includes designing software programs and big data tools that address the specific needs of their employers. They collaborate with other data professionals to create unique machine learning frameworks and implement them as needed. They perform tests and ensure the frameworks are running smoothly.

Machine learning engineer roles usually require a bachelor’s degree and sometimes a master’s degree in subjects such as computer science or engineering. It’s helpful to have a background in computer programming languages and experience with analyzing big data sets. 

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

3. Computational linguist

Average annual base salary (US): $116,130 [3]

Computational linguists build machine learning frameworks that use math to read, understand, and translate different languages. They design applications that make it easier for technology to interact with humans and communicate effectively. Computational linguists exist where language processing and artificial intelligence meet. They use their skills in both technology and language to create software that makes it easier for people to communicate effectively with automated technology and each other. Computational linguists use voice recognition, automated text analysis, search engines, and translation.

On a typical day, a computational linguist might help design software that understands human speech and then troubleshoot any issues that may come up. They also research speech patterns to help make their algorithms more accurate.

Computational linguists typically need a bachelor’s degree in computer science. It’s important to have foundational knowledge about data and algorithms. 

4. Software developer

Average annual base salary (US): $105,920 [4]

Software developers create software programs based on their company’s unique needs and goals. They design, code, implement, and maintain different software programs, all while working with other data professionals. They create this software for specific tasks, such as data analysis.

A typical day as a software developer includes meeting with other professionals to learn about the specific needs and goals of their software. Then, they build, launch, troubleshoot, and maintain the software itself.

Software developers usually need to have a bachelor’s degree in computer science, software engineering, or an adjacent field. Employers tend to prefer hires with experience in different programming languages. Some software developers choose to pursue certification to make themselves more competitive.

Read more: How to Become a Software Developer (With or Without a Degree)

Get started with Coursera

Sharpen your machine learning skills and gain foundational knowledge through courses and certificates on Coursera. With choices like Stanford and DeepLearning.AI’s Machine Learning Specialization, you’ll learn about the key pieces of working with machine learning and what it takes to start a career in machine learning. This program will cover supervised machine learning, learning algorithms, reinforcement learning, and more. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.

Article sources


Glassdoor. “What does a Data Scientist Do?, https://www.glassdoor.com/Career/data-scientist-career_KO0,14.htm#:~:text=%24129%2C590,%C2%A0/yr.” Accessed February 7, 2024.

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