What is Machine Learning?
First, let’s explore the definition of machine learning, which is categorized as a subsection (or type) of artificial intelligence. Machine learning is “all about extracting valuable information from data” — which could be anything from text and video to images and sound. As the name implies, the ability to learn is what separates machine learning from other AI subsets.
“Machine learning is a branch of AI that gives computer systems the ability to automatically learn and improve from experience, rather than being explicitly programmed. In machine learning, computers use massive sets of data and apply algorithms to train on and make predictions,” according to TechRepublic.
When it comes to real-life applications, tech site ZDNet reports, “From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence – helping software make sense of the messy and unpredictable real world.”
According to MIT Technology Review, “Machine learning is the process that powers many of the services we use today — recommendation systems like those on Netflix, YouTube and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.”
What Does a Machine Learning Professional Do?
There are several types of machine learning professionals, and some job responsibilities may overlap with other roles, depending on the size of an organization. In general, working with machine learning deals with complex challenges and often includes the following job duties:
- Continuously monitoring and updating ML models to adapt to new data
- Researching and developing ML algorithms
- Implementing ML algorithms in various applications
- Running experiments and tests on AI systems to evaluate functionality and efficiency
- Designing and developing scalable ML systems
- Performing statistical analyses to refine models and improve accuracy
- Collaborating with data scientists and software engineers to integrate ML models into production environments
- Analyzing large datasets to extract meaningful patterns and insights
- Continuously monitoring and updating ML models to adapt to new data
“People interested in machine learning are often problem solvers,” according to U.S. News & World Report. “They are motivated by the challenge of finding patterns that others can’t see. They develop tools that can sift through huge datasets and find the commonalities. They want to turn the unknown into the known and help make better decisions and produce better results.”
One of the top roles among AI professionals is the machine learning engineer, whose responsibilities include building and managing platforms for ML projects. As Discover Data Science explains, machine learning engineers may often double as data scientists at smaller organizations.
“The responsibilities of a machine learning engineer will be relative to the project they’re working on,” according to Springboard.com. “However, if you explore the job postings, you’ll notice that for the most part, machine learning engineers will be responsible for building algorithms that are based on statistical modeling procedures and maintaining scalable machine learning solutions in production.”
Read on for insights into key machine learning skills and the machine learning engineer career path.
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Skills for Machine Learning Professionals
According to TechRepublic, machine learning engineers must be skilled in the following:
- Computer science fundamentals
- Programming
- Math and statistics
- Data science
- Deep learning
- Problem solving
- Software engineering and system design
- Artificial intelligence
Soft skills are also extremely valuable in machine learning positions. These include:
- Teamwork
- Good communication
- Organization
- Strong work ethic
How to Start a Career in Machine Learning
Undergraduate degree
The first step toward establishing a career in machine learning is to secure an undergraduate degree in computer science, mathematics, data science, computer programming or related field.
Experience
As described by Discover Data Science, some machine learning positions may require previous experience in an engineer or artificial intelligence-specific role. For example, it’s important to know that a machine learning engineer is not an entry-level position, but one that often requires experience in data science and software engineering — plus an advanced degree. For a career in machine learning, you may want to start in a software engineer, software programmer, software developer, data scientist or computer engineer role.
Advanced degree
For most machine learning careers, an undergraduate degree will not suffice. A master’s in computer science, software engineering, artificial intelligence or machine learning can expand your knowledge and skills, and advance your career. Core machine learning courses may include computer science, high-level mathematics, applied machine learning, statistics, research, data analysis, artificial intelligence and more.
An advanced degree in artificial intelligence can prepare students for a wider range of AI- and ML-related opportunities.
8 Career Paths in Machine Learning
| Career | Description | Median Total Pay* |
| Machine Learning Engineer | Builds and manages platforms for machine learning projects. | $168,730 |
| Data Scientist | Collects, analyzes and interprets complex sets of data by using machine learning and predictive analytics. | $165,029 |
| Natural Language Processing (NLP) Scientist | Involves “developing novel NLP algorithms and techniques to solve problems like information extraction, text summarization, natural language generation, automated question answering, etc.” | $180,727 |
| Business Intelligence (BI) Developer | Analyzes data sets for business and market trends. | $126,174 |
| Software Engineer | Researches, designs, implements and supports software solutions. Oversees the whole system and uses engineering concepts to develop software. | $162,927 |
| Software Developer | Responsible for the entire software development process. | $134,584 |
| Computational Linguist | Teaches computers how to understand human language. | $128,261 |
Figures listed are accurate as of February 2025. Salary data fluctuates frequently.
Machine Learning Job & Salary Outlook
The job outlook for machine learning — and artificial intelligence in general — is extremely promising. A February 2025 LinkedIn search for “machine learning engineer” returned over 2,800 results, including positions at companies such as Amazon, Coinbase, TikTok, Pinterest, Ford Motor Company, Netflix, Spotify and Adobe.
Machine learning and AI specialists are making a huge impact on the global workforce. Indeed lists machine learning engineer as #8 among The Best Jobs in the U.S., citing a 53% growth rate since 2020. With computer and information technology jobs projected to grow much faster than the average for all occupations from 2022 to 2032, the demand for AI and machine learning specialists is expected to rise by 40% — or 1 million jobs — over the next five years, according to the World Economic Forum’s Future of Jobs Report 2023. This significant growth underscores a bright machine learning jobs future.
This high demand for machine learning talent translates into competitive salaries. According to Glassdoor, the average annual salary for a machine learning engineer in the United States is $168,730, with salaries ranging from $135,000 to $215,000.
Companies Hiring Machine Learning Professionals
On LinkedIn, the long list of companies seeking machine learning professionals features some of the biggest names in business, as well as companies serving a wide variety of industries. Here is a sample:
| Workday | Amazon | |
| Capital One | Brown University | Microsoft |
| Liberty Mutual | Spotify | Meta |
| TikTok | Boston Children’s Hospital | Reebok |
| Wayfair | Mayo Clinic | Major League Soccer |
| National Football League | Massachusetts Institute of Technology | CarMax |
| FDA | Target | Sephora |
Machine Learning Career Landscape
The machine learning opportunities available today are vast and varied, providing ample room for growth and advancement. Video surveillance, smartphone traffic alerts, facial recognition software, personalized product recommendations on website browsers — these are all examples of real-life machine learning applications. This important field appeals to many due to its potential for technological breakthroughs and lucrative career opportunities.
The machine learning career landscape offers a variety of paths for professionals at different stages of their careers. Whether you aspire to become a machine learning engineer or explore other ML-related career opportunities, the future in this field is promising and full of potential.
Frequently Asked Questions
Take Your Machine Learning Career to the Next Level with an Advanced Degree
High-paying career opportunities in AI, machine learning and related disciplines continued to expand across a variety of industries. An advanced degree in artificial intelligence can position you for success and provide a competitive edge in the growing artificial intelligence job market, which includes machine learning.
The University of San Diego — a highly regarded industry thought leader and education provider — offers an innovative, online AI master’s degree program, the Master of Science in Applied Artificial Intelligence, which is designed to prepare graduates for success in this important, fast-growing field. This program includes a significant emphasis on real-world applications, ethics, privacy, moral responsibility and social good in designing AI-enabled systems.
