Machine Learning Engineer: Salary and Job Outlook

From predictive algorithms for customer service chatbots to autonomous driving systems, the technology that machine learning engineers work on helps shape the future of innovation.

Like data scientists, machine learning engineers work extensively with large datasets. But, though they use similar tools, data scientists focus on extracting insights from data, while machine learning engineers build scalable models that learn from data to improve their performance or generate predictions.

Competitive machine learning engineer salaries and a promising job outlook make it a popular career choice for those with the right education and background. Companies, especially large enterprises, are actively hiring machine learning engineers, with McKinsey & Co. reporting that in 2024 about 60% of organizations considered the role difficult to fill.

Both machine learning engineers and data scientists can benefit from gaining a strong foundation in the field by earning an advanced degree in data science.

What Does a Machine Learning Engineer Do?

Professionals working in machine learning, a branch of artificial intelligence (AI), focus on building systems that can learn independently without being explicitly programmed, instead improving themselves over time. Machine learning engineers use their background in computer science, math, and statistics to develop and update machine learning models and algorithms.

Machine learning engineers manage large datasets, which is why a background in data analytics and data modeling can provide aspiring machine learning engineers with useful skills. While their work can vary significantly depending on the organization they work for and its specific needs, it typically involves research, development, and hands-on implementation.

Machine Learning Engineer Job Description

Machine learning engineering roles go by different titles, and the daily tasks for each can vary. For instance, a machine learning research scientist’s responsibilities may differ from those of a software engineer or algorithm engineer. But their goal is the same: to support AI system development and refinement.

Typical machine learning duties include:

• Assessing AI performance by running tests
• Improving algorithms based on test results
• Troubleshooting AI system issues
• Designing and implementing machine learning models
• Selecting and preparing datasets
• Verifying data quality and refining prototypes
• Performing statistical analysis and model evaluations

How to Become a Machine Learning Engineer

Most machine learning engineer job descriptions include working with cloud platforms, machine learning frameworks, and data science. But the path to becoming a machine learning engineer is not always linear. Following are the skills, education, and experience most needed for success as a machine learning engineer.

Skills

Typical machine learning engineering positions require skills in the following areas:

• Programming languages. Python, R, and Java are the primary programming languages used by machine learning engineers. Python is the most widely used, and R is particularly important for statistical analysis. Java is often used in production environments where performance and scalability are priorities. Understanding software design and knowing how to use machine learning frameworks and tools, such as Apache Spark MLlib, TensorFlow, PyTorch, Scikit-Learn, Keras, Shogun, and Rapid Miner, are also important.
• Mathematics. Statistics helps machine learning engineers understand data distributions, evaluate model effectiveness, and make decisions on design. Linear algebra and calculus form the foundation for understanding machine learning algorithms, which is particularly important for working with deep learning and neural networks.
• General business skills. Creativity, clear communication skills, and effective problem-solving skills enable machine learning engineers to optimize model performance and translate business needs into technical solutions. Being skilled at teamwork is also a major part of the job, as machine learning engineers often work closely with data scientists, software developers, and product teams.

Education

Most machine learning roles require at least a bachelor’s degree in computer science, data science, mathematics, statistics, engineering, or a related field. Some positions may require a graduate degree in data science or AI engineering. An advanced degree or a certification can help candidates stand out to employers. Some valuable certifications are AWS’s certified machine learning engineer credential and Google’s professional ML engineer credential.

Experience

Experience as a software engineer or developer can help professionals make the transition to machine learning roles. Experience working on teams where software and machine learning specialists collaborate can help individuals build relevant skills, including in areas such as programming (Python, JavaScript, C#), databases, application programming interfaces (APIs), version control, testing, and agile methods.

A background in linear algebra, calculus, and probability/statistics, plus hands-on experience from AI/machine learning hackathons and a well-documented GitHub portfolio, also can help individuals demonstrate readiness for machine learning opportunities.

Machine Learning Engineer Salary and Job Outlook

The median annual machine learning engineer salary was approximately $123,000 as of August 2025, according to Payscale. Entry-level machine learning engineers had a median salary of around $102,000 per year, and those with 10 to 19 years of experience had a median salary of around $158,000.

Specializing in specific skills can also affect an individual’s salary. For instance, machine learning engineers with software development expertise had a median salary of around $125,000, and for those with data modeling skills it was around $131,000. Location is another factor affecting salaries — machine learning engineers in San Jose, California, earned 20% more than the national average, while those in Chicago, Illinois, earned about 7% less, according to Payscale data.

The job market in the field is remarkably strong. The U.S. Bureau of Labour Statistics predicts that positions for data scientists, including machine learning engineers, will grow 36% between 2023 and 2033, vastly outpacing the labor market as a whole.

Advance in Your Career in Data Science and Machine Learning

Data is crucial to machine learning applications, and machine learning engineers must know how to prepare data for use in models, ensure the quality of the data they use, and standardize data formats. Engineers who want to lead the next generation of machine learning technology can enhance their skills and expand their knowledge to appeal to employers.

The online Master of Science (MS) in Data Science program offered by Tseng College at California State University, Northridge (CSUN), helps students develop practical skills in programming, data engineering, and machine learning while learning to solve real-world problems using large datasets. Students learn the skills required to select and apply models and algorithms, extract meaningful insights, and communicate their findings through effective data visualization — all skills that are in high demand across industries.

CSUN’s MS in Data Science is a two-year online program designed to prepare students with the diverse skill set top employers seek for roles in machine learning, data science, and other AI-related fields. Take charge of your future with an advanced degree from CSUN.