Machine Learning Engineers are at the forefront of the artificial intelligence revolution, building the intelligent systems that are transforming industries. These professionals specialize in creating and deploying models trained on vast datasets, enabling machines to predict outcomes and adapt dynamically. The demand for skilled machine learning engineers is surging, with the World Economic Forum projecting a significant 40% growth in job opportunities by 2027 [1].
If you’re curious about a career in this exciting and impactful field, this article will provide a comprehensive overview of what machine learning engineers do, their earning potential, and the steps you can take to become one. For those eager to jumpstart their journey, consider exploring Microsoft’s AI & Machine Learning Engineering Professional Certificate on Coursera. This program offers hands-on experience in building, deploying, and innovating with cutting-edge machine learning technologies and real-world projects.
What is Machine Learning?
Machine learning is a specialized area within computer science and artificial intelligence that focuses on developing algorithms that learn from data. This learning process mirrors human learning, where algorithms progressively refine their accuracy as they are exposed to datasets. The ultimate objective is to train these algorithms to execute specific tasks effectively [2].
Machine learning’s applications are incredibly diverse, ranging from sophisticated video surveillance systems to the facial recognition technology on your smartphone. Businesses across various sectors leverage machine learning to gain deep insights into consumer behavior, understand purchasing patterns, and tailor marketing strategies for maximum impact.
Consider the personalized experiences offered by tech giants. Social media platforms like Meta utilize machine learning to deliver targeted advertising based on user preferences and online activity. Similarly, e-commerce leaders like Amazon employ algorithms to recommend products to customers, drawing from their purchase history and browsing behavior [3].
What Does a Machine Learning Engineer Do?
Machine learning engineers are vital members of any data science team. Their core responsibilities revolve around the research, construction, and design of machine learning systems that power artificial intelligence. They are also tasked with the ongoing maintenance and enhancement of these systems.
A key aspect of a machine learning engineer’s role is communication. They often bridge the gap between data scientists, who develop the theoretical models for AI, and the teams responsible for the practical implementation and operation of these systems. While specific duties can vary depending on the organization, common responsibilities include:
- Implementing sophisticated machine learning algorithms
- Conducting rigorous experiments and tests on AI systems
- Designing and building end-to-end machine learning systems
- Performing in-depth statistical analyses to refine models
Job Outlook for Machine Learning Engineers
The field of computer science has experienced remarkable growth in recent decades, and machine learning is at the heart of this expansion. The US Bureau of Labor Statistics projects a robust 26 percent growth in information and computer science research roles through 2033, significantly outpacing the average growth rate for all occupations [4].
Reflecting this trend, Indeed recognized machine learning engineer as a top 10 job in 2023. This ranking was based on the substantial increase in job postings related to machine learning and artificial intelligence over the preceding three years [5]. As AI systems become increasingly sophisticated, the demand for automation of routine tasks is reaching unprecedented levels, further fueling the need for machine learning expertise.
Machine Learning Engineer Salary
Careers in machine learning are not only in high demand but also exceptionally rewarding financially. Machine learning professionals typically command high salaries, with compensation scaling based on experience and geographic location. Consistent with other advanced technology and computer science positions, machine learning engineers often earn salaries well into the six-figure range. Indeed data from January 2025 indicates an average base salary of $162,297 for machine learning engineers [6].
How to Become a Machine Learning Engineer
There are several pathways to a fulfilling career as a machine learning engineer. Let’s outline three essential steps to guide you on this journey:
1. Earn a Bachelor’s Degree in Computer Science or a Related Field
Given that machine learning is a subfield of computer science, a strong foundation in computer programming, data science principles, and mathematics is crucial. A bachelor’s degree is often the minimum educational requirement for machine learning engineering roles. Pursuing a degree in computer science or a closely related field like statistics or data science is an excellent starting point.
2. Gain Entry-Level Work Experience
After completing your computer science degree, the next step is to gain practical experience in the data science domain, specifically working with machine learning or artificial intelligence technologies. Entry-level roles that can serve as stepping stones to a machine learning career include data analyst, junior data scientist, AI developer, or software engineer with a focus on AI applications.
3. Build Your Machine Learning Expertise
While gaining experience in a related role, it’s essential to deepen your specialized knowledge and refine your skill set in machine learning. Consider enrolling in targeted machine learning programs and professional certificates to continuously expand your expertise. These programs can provide focused training and credentials to enhance your qualifications.
Do You Need an Advanced Degree to be a Machine Learning Engineer?
While a bachelor’s degree can open doors in data science and AI, pursuing a master’s degree in computer science, data science, or software engineering can significantly enhance your career prospects as a machine learning engineer. An advanced degree provides you with a deeper understanding of complex machine learning concepts and techniques, making you a more competitive candidate, particularly when combined with relevant industry experience gained through internships or apprenticeships.
Build Your Machine Learning Skills on Coursera
The fields of artificial intelligence and machine learning are rapidly expanding, presenting significant career opportunities. Equip yourself with the necessary skills to thrive in this in-demand profession by exploring these courses on Coursera:
For foundational machine learning skills, consider the renowned Machine Learning Specialization from DeepLearning.AI and Stanford University. This beginner-friendly program will guide you through building ML models, applying best practices in ML development, and even constructing and training your own neural network for multi-class classification tasks.
To prepare specifically for a machine learning engineer career path, enroll in the Microsoft AI & ML Engineering Professional Certificate. This intermediate-level program will enable you to design and implement AI and ML infrastructure, master essential machine learning algorithms and techniques, and develop your own AI-powered agent.
For an in-depth exploration of AI engineering, delve into IBM’s AI Engineering Professional Certificate. In approximately three months, you can gain a solid grasp of AI fundamentals, deploy practical machine learning algorithms, and build sophisticated deep learning models and neural networks.
Embark on your journey to become a machine learning engineer and contribute to the future of technology. Explore these resources and take the first step towards a rewarding and impactful career.
