How to Become a Machine Learning Engineer for 2025

Co-Founder and Chief Data Scientist

Employment for professionals in the field of technology has been expected to increase significantly over recent years. For instance, software development roles across the United States (US) have been forecasted to grow by 18% between 2023 and 2033, which is much faster than the average job growth rate for all occupations in the US [US Bureau of Labor Statistics (BLS), 2024]. Thus, if you aim to become a machine learning engineer, you can expect to have various career opportunities available to you.

To help you venture into the field, I created this in-depth guide to becoming a machine learning engineer. Explore the responsibilities of machine learning engineers, key skills you are required to develop, and the education and training you must undergo to succeed, among other key information.

What are the benefits of becoming a machine learning engineer?

A machine learning engineer can qualify for several other roles in the field of technology, such as AI researcher, data scientist, deep learning specialist, robotics engineer, and software developer.
In 2023, data scientists in the US had a median annual wage of $108,020, meanwhile computer programmers had $99,700 (US BLS, 2024).
Machine learning engineers can find work in technology companies, financial organizations, manufacturing, as well as academic and research institutions, among other industries.

What does a machine learning engineer do?
What are the key skills required to become a machine learning engineer for 2025?
What degree do you need to become a machine learning engineer?
Can you get a job in machine learning with just an online course?
How can you build a strong portfolio for a machine learning job?
What are the best certifications for machine learning engineers?
What are the biggest challenges when learning machine learning?
Can an accelerated online computer science degree fast-track a machine learning career?
Can interdisciplinary studies enhance machine learning engineering success?
Can interdisciplinary expertise, including health informatics, enhance machine learning careers?
Can an advanced degree accelerate a machine learning engineering career?
What emerging trends are shaping the future of machine learning engineering?
How Can Soft Skills and Interdisciplinary Expertise Propel a Machine Learning Career?
How much do machine learning engineers make on average?
What industries are hiring machine learning engineers?
What is the career growth potential for machine learning engineers?
Other Things You Should Know about How to Become a Machine Learning Engineer

What does a machine learning engineer do?

A machine learning engineer is trained to develop AI-driven solutions by designing, building, and optimizing machine learning models. Many professionals enter this field by earning an online engineering degree, which provides foundational knowledge in programming, algorithms, and data science. The role involves various technical and strategic tasks, including the following:

Data Collection and Preprocessing: Machine learning engineers gather, clean, and prepare large datasets for training models. They handle missing data, remove inconsistencies, and ensure the dataset is properly formatted for analysis.
Developing and Training Models: They design algorithms, select appropriate machine learning techniques, and train models to make accurate predictions. This process involves fine-tuning hyperparameters and optimizing model performance.
Deploying and Maintaining Models: After training, models must be integrated into production environments. Engineers ensure smooth deployment, monitor model performance, and retrain models as data evolves.
Collaborating with Cross-functional Teams: Machine learning engineers work closely with data scientists, software engineers, and business analysts to align AI solutions with organizational goals.
Ensuring Model Interpretability and Ethics: They address biases, ensure fairness, and comply with ethical AI guidelines while making models transparent and understandable to stakeholders.

This multifaceted role requires a balance of technical expertise and strategic insight, and shares foundational competencies with roles detailed in our computer engineer career guide, particularly in areas like algorithm design, systems thinking, and software integration.

What are the key skills required to become a machine learning engineer for 2025?

To succeed as a machine learning engineer, you need a combination of technical expertise and problem-solving abilities. Earning an online software engineering degree can help build the necessary skills for this field. During my research, I found that the following are three essential skills:

Programming and Software Development: Proficiency in programming languages, such as Python or Java, is crucial for implementing machine learning algorithms. Engineers must also understand software engineering principles, version control, and API development to integrate models into real-world applications.
Mathematics and Statistics: A strong foundation in linear algebra, probability, calculus, and statistics is essential for understanding machine learning models. These concepts help engineers optimize algorithms, analyze model accuracy, and fine-tune hyperparameters for better performance.
Machine Learning Algorithms and Frameworks: Engineers must be skilled in popular machine learning techniques, including supervised and unsupervised learning, neural networks, and deep learning. Familiarity with frameworks enables efficient model development and deployment.

These competencies blur the line between development and engineering, making the question of software developer vs software engineer especially relevant for those entering the machine learning space. Machine learning engineers must not only write code but also architect scalable systems—an intersection of responsibilities that spans both roles.

What degree do you need to become a machine learning engineer?

A bachelor’s degree in computer science, data science, mathematics, or engineering is usually required to become a machine learning engineer. This provides foundational knowledge in programming, algorithms, and statistics. However, some professionals transition from related fields, such as physics. Notably, engineering is among the six fields of study that are the most conferred in the US. In the academic year 2021–2022, 6%, or 123,000 degrees, of the 2 million bachelor’s degrees awarded across the country were engineering degrees (National Center for Education Statistics, 2024).

An online software development degree can also provide a strong foundation and equip students with coding, algorithm design, and data structure expertise—key skills for a machine learning engineer.

A master’s degree in artificial intelligence, machine learning, or data science can enhance career prospects. Graduate degree programs offer specialized training in deep learning, natural language processing, and big data analytics. While a PhD is beneficial for research-oriented roles, it may not be necessary for most industry positions.

Self-taught learners can enter the field through online courses, bootcamps, and certifications. Practical experience, strong coding skills, and a portfolio of projects are often considered more than formal education when securing a job in the field.

Can you get a job in machine learning with just an online course?

An online course alone is usually not enough to land a job as a machine learning engineer, but it can be a great starting point. Employers look for practical experience, strong programming skills, and a solid understanding of machine learning algorithms beyond just theoretical knowledge.

To improve job prospects, candidates should supplement online courses with hands-on projects, internships, and open-source contributions. Building a portfolio showcasing real-world applications, such as predictive models or deep learning projects, can significantly boost credibility.

Moreover, pursuing AI degrees online can provide a more structured and in-depth education, covering advanced topics like deep learning, neural networks, and big data analytics. Earning certifications from different reputable learning platforms, including Coursera, Udacity, or TensorFlow, can enhance resumes. While an online course provides foundational knowledge, practical application and continuous learning are essential for securing a machine learning role.

How can you build a strong portfolio for a machine learning job?

A strong portfolio is essential for showcasing practical skills and real-world experience in machine learning. Earning the cheapest online IT degree can provide a cost-effective way to build foundational skills in programming, data structures, and algorithms, which are crucial for a machine learning career. The following steps can help guide you in building your portfolio effectively:

Work on diverse projects. Include projects covering supervised and unsupervised learning, deep learning, natural language processing, and computer vision to demonstrate versatility.
Use real-world datasets. Utilize datasets from Kaggle, UCI Machine Learning Repository, or real business cases to make projects more impactful.
Contribute to open source. Engage in GitHub projects, Kaggle competitions, or AI communities to gain credibility.
Deploy models. Showcase end-to-end solutions by deploying models on Flask, FastAPI, or cloud platforms, such as Amazon Web Services (AWS) or Google Cloud.
Write technical blogs. Share insights on Medium, personal blogs, or LinkedIn to highlight expertise and attract potential employers.

While the focus here is on machine learning, building a strong portfolio is equally vital in other areas of software development. For instance, understanding how to become a full stack developer involves not only mastering both front-end and back-end technologies but also demonstrating your skills through comprehensive projects. Similarly, aspiring machine learning engineers should aim to present a diverse range of projects that highlight their proficiency across various aspects of the field.

What are the best certifications for machine learning engineers?

Certifications can enhance a machine learning engineer’s resume by validating expertise in AI, deep learning, and data science. They demonstrate proficiency in key industry-standard tools. While not always required, certifications can help career changers and entry-level professionals stand out in a competitive job market. A few examples of machine learning certifications you can explore are:

Google Cloud Professional Machine Learning Engineer: Google Cloud offers its Professional Machine Learning Engineer credential to professionals who are skilled in creating, assessing, and optimizing AI solutions with the use of machine learning techniques and Google Cloud features. Aspirants are required to pass a 2-hour certification examination with multiple choice and multiple select questions. You may benefit from having at least 3 years of industry experience, with a minimum of 1 year using Google Cloud.
Amazon Web Services (AWS) Certified Machine Learning Engineer: AWS offers associate and specialty certifications in machine learning. The AWS Certified Machine Learning Engineer-Associate credential assesses technical skills in machine learning applications through a 130-minute certification examination. Meanwhile, the AWS Certified Machine Learning - Specialty helps companies determine and hone talent for cloud initiatives through a 180-minute certification examination.
IBM Machine Learning Professional Certificate: IBM offers a Machine Learning Professional Certificate, which evaluates individuals in major areas of machine learning, including supervised learning, unsupervised learning, deep learning, and reinforcement learning.

What are the biggest challenges when learning machine learning?

Being a beginner at machine learning can be challenging due to its complexity and multidisciplinary nature. The following are three common obstacles that learners may face:

Mathematical Complexity: Machine learning relies heavily on linear algebra, calculus, probability, and statistics. Understanding concepts, such as gradient descent, optimization algorithms, and matrix operations, can be difficult for those without a strong math background.
Lack of Practical Experience: Many beginners focus too much on theory without working on real-world projects. Without hands-on coding, data preprocessing, and model deployment, it can be difficult to grasp the practical applications of machine learning.
Overwhelming Number of Tools and Libraries: The machine learning ecosystem includes numerous frameworks, such as TensorFlow, PyTorch, and Scikit-learn, each with different functionalities. Choosing the right tools and keeping up with evolving technologies can be overwhelming.

Can an accelerated online computer science degree fast-track a machine learning career?

Pursuing an accelerated pathway can provide professionals with a concentrated curriculum that emphasizes both theoretical foundations and practical applications. This approach supports rapid skill acquisition, helps align educational outcomes with industry demands, and enables quicker entry into competitive roles. For candidates seeking to expedite their educational journey while maintaining a strong technical foundation, an accelerated online computer science degree offers a streamlined route to mastering concepts critical to machine learning, such as advanced algorithms, big data analytics, and system optimization.

Can interdisciplinary studies enhance machine learning engineering success?

Integrating insights from diverse fields can foster innovative approaches in machine learning engineering. Cross-disciplinary study not only augments technical competencies but also cultivates creative problem-solving skills essential for designing user-centric AI systems. For instance, exploring artistic and design-oriented disciplines can offer fresh perspectives that complement analytical expertise, thereby expanding the range of effective solution strategies. Educational pathways that include non-traditional subjects may provide a competitive edge in the rapidly evolving tech landscape, as demonstrated by programs like the online game design degree, which encourages innovative thinking in digital content creation.

Can interdisciplinary expertise, including health informatics, enhance machine learning careers?

Integrating expertise from additional disciplines offers machine learning engineers a competitive edge by broadening their problem-solving capabilities and opening new industry avenues. Professionals who develop skills in fields such as health informatics can tailor AI solutions to address sector-specific challenges, ultimately fostering innovation and improved outcomes. By pursuing specialized courses or certifications in these interdisciplinary areas, engineers can position themselves to tackle complex, real-world problems and meet emerging market demands. For example, insights into the job outlook for health informatics can reveal growing opportunities at the intersection of technology and healthcare, enabling a more diversified and future-ready career trajectory.

Can an advanced degree accelerate a machine learning engineering career?

Advanced academic credentials can significantly enhance a candidate’s expertise and leadership potential. Graduate programs offer deeper specialization and research opportunities that foster innovative problem-solving and mastery of emerging algorithms. Such credentials not only refine technical proficiency but also expand professional networks and open pathways to higher-level roles. For professionals seeking a flexible pathway to advanced learning, enrolling in affordable online masters in artificial intelligence provides an opportunity to align academic pursuits with industry demands and accelerate career progression.

What emerging trends are shaping the future of machine learning engineering?

Rapid advancements in AI research and deployment are continuously redefining the machine learning engineering landscape. Notable trends include the rise of automated machine learning (AutoML) and MLOps platforms, increased integration of edge computing for real-time data processing, and growing emphasis on ethical AI practices that address data privacy and bias. Professionals are also witnessing a shift toward interdisciplinary applications, where insights from fields such as healthcare, finance, and smart infrastructure drive innovative solutions. Staying competitive requires continuous engagement with evolving technologies and specialized training, such as pursuing an affordable online master's in data science, to deepen expertise and adapt to emerging industry demands.

How Can Soft Skills and Interdisciplinary Expertise Propel a Machine Learning Career?

Developing technical proficiency is only one aspect of success in machine learning engineering. Equally important is the cultivation of soft skills such as effective communication, teamwork, and problem-solving, which enable professionals to articulate complex ideas and collaborate across diverse domains. Establishing a broad network and engaging in continuous professional development—through workshops, mentoring programs, and cross-industry projects—can enhance your ability to lead interdisciplinary initiatives. For those looking to expand their expertise into related fields like healthcare, exploring opportunities such as cheap nursing informatics programs online can foster innovative solutions and further broaden career prospects.

How much do machine learning engineers make on average?

An aspiring machine learning engineer or a related professional can have a high income potential. Notably, earning potential depends on several factors, including the following:

Experience Level: Experienced professionals earn significantly more than entry-level professionals.
Industry: High-paying sectors include technology, finance, and healthcare.
Location: Salaries in major technology hubs, such as San Francisco and New York, tend to be higher.
Skills and Certifications: Expertise in AI frameworks, cloud computing, and big data can boost earning potential.

Notably, the 2023 median annual wages of professions related to machine learning engineering is considerably higher than that of all occupations across the US (US BLS, 2024):

Data Scientists: $108,020
Software Developer: $132,270
Software Quality Assurance Analysts and Testers: $101,800
Computer Programmers: $99,700
Computer and Information Research Scientists: $145,080

The chart below provides a visualization of the 2023 median annual wages of jobs related to machine learning engineering, according to 2024 data from the US BLS.

What industries are hiring machine learning engineers?

Machine learning engineers and related roles are in demand across multiple industries, particularly those leveraging AI for automation, data analysis, and predictive modeling. For instance, in 2023, the largest employers of data scientists across the US were the following (US BLS, 2024):

Computer Systems Design (11%): Tech companies develop AI-powered applications, cloud computing solutions, and cybersecurity systems.
Insurance (10%): Insurance firms use machine learning for fraud detection, risk assessment, and customer analytics.
Corporate Management (9%): Corporate enterprises apply AI for business intelligence, automation, and operational efficiency.
Consulting Services (6%): AI-driven strategies help consultancies optimize performance and decision-making.
Scientific Research (5%): Healthcare, pharmaceuticals, and engineering fields leverage AI for drug discovery, medical imaging, and robotics.

The chart below provides a visualization of the largest employers of data scientists in the US in 2023, according to 2024 data from the US BLS.

What is the career growth potential for machine learning engineers?

Machine learning engineers have strong career growth potential, as AI adoption continues to expand. Entry-level engineers can advance to senior engineer, AI architect, or research scientist roles. With experience, professionals may move into leadership positions, such as team lead or AI director. Specializing in deep learning, natural language processing (NLP), or machine learning operations (MLOps) can further boost opportunities.

Pursuing an online master of computer science can also enhance career prospects by providing advanced technical training and research opportunities. As industries integrate AI-driven solutions, demand for skilled machine learning engineers is expected to grow, ensuring long-term job stability and high earning potential.

During my research for this guide, I found the job outlook for professions related to machine learning engineering in the US between 2023 and 2033 (US BLS, 2024):

Data Scientists: Employment for data scientists across the country has been projected to increase by 36% between 2023 and 2033, which translates to an average of around 20,800 job openings annually during the decade.
Software Developer, Quality Assurance Analysts, and Testers: Software developers have also been projected to see a positive job growth rate over the decade, with 18%. Meanwhile, employment for software quality assurance analysts and testers is expected to grow by 12% during the same period. The job outlook for software developers, software quality assurance analysts, and testers in the US translates to around 140,100 job openings annually.
Computer Programmers: Unfortunately, computer programmers have a negative rate, with -10%, but around 6,400 job openings for the role have still been projected yearly.
Computer and Information Research Scientists: Computer and information research scientists have a positive job growth rate, with 26%, translating to about 3,400 job openings annually.

For those interested in expanding their technical expertise beyond machine learning, a career in cybersecurity offers a compelling option. Understanding how to become an information security analyst provides an opportunity to apply similar problem-solving skills while focusing on protecting systems and data from evolving cyber threats. With AI-driven solutions becoming increasingly integrated into security practices, professionals in both fields share common ground in their need for advanced technical knowledge, risk assessment, and systems optimization. The growing demand for cybersecurity professionals, combined with the rise of AI applications, ensures both machine learning engineering and information security offer promising career paths with long-term growth potential.

Here’s What Professionals Have to Say about Their Careers in Machine Learning Engineering

“Becoming a machine learning engineer has been the most rewarding decision of my career. The ability to build models that drive real-world impact—whether in healthcare, finance, or autonomous systems—is both exhilarating and intellectually stimulating. Every day presents a new challenge, pushing me to stay curious and continuously learn.” —Jacob
“The best part about being a machine learning engineer is the fusion of creativity and logic. I get to experiment with data, fine-tune algorithms, and see my models make accurate predictions that improve business decisions. The demand for ML skills is skyrocketing, and the opportunities for growth are endless.” —Patricia
“Transitioning into machine learning was challenging, but the journey was worth it. I started with a background in software development, upskilled through online courses, and now work on cutting-edge AI projects. Seeing my models evolve from raw data to real-world applications gives me an incredible sense of accomplishment.” —Julia

Key Findings

Engineering is among the six fields of study that are the most conferred in the US. In the academic year 2021–2022, 6%, or 123,000 degrees, of the 2 million bachelor’s degrees awarded across the country were engineering degrees (National Center for Education Statistics, 2024).
An online course alone is usually not enough to land a job as a machine learning engineer, but it can be a great starting point. Employers look for practical experience, strong programming skills, and a solid understanding of machine learning algorithms beyond just theoretical knowledge.
Google Cloud offers its Professional Machine Learning Engineer credential to professionals who are skilled in creating, assessing, and optimizing AI solutions with the use of machine learning techniques and Google Cloud features. Meanwhile, AWS offers associate and specialty certifications in machine learning.
The 2023 median annual wages of professions related to machine learning engineering is considerably higher than that of all occupations across the US. For instance, data scientists had $108,020, software developers had $132,270, and computer and information research scientists had $145,080 (US BLS, 2024).
Employment for data scientists across the country has been projected to increase by 36% between 2023 and 2033, which translates to an average of around 20,800 job openings annually during the decade (US BLS, 2024).

Other Things You Should Know about How to Become a Machine Learning Engineer

Is it hard to become an ML engineer?

Becoming a machine learning engineer is challenging but achievable with dedication. It requires mastering programming, math, data science, and artificial intelligence (AI) concepts. While the learning curve is steep, structured study, hands-on projects, and real-world experience can make the journey manageable. Persistence and continuous learning are key to success in this evolving field.

Do ML engineers get paid well?

Machine learning engineers are among the highest-paid tech professionals. As of June 2024, machine learning engineers in the US have an average annual salary of $123,734 (Glassdoor, 2024). Different factors, such as experience, location, and industry impact earnings. High demand for AI expertise ensures competitive compensation and strong career growth opportunities.

Are ML engineers in demand?

Machine learning engineers are in high demand across industries, including technology, healthcare, finance, and automation. With AI adoption growing, companies seek experts to develop intelligent systems. The US BLS predicts strong job growth in related professions, making machine learning a lucrative and future-proof career choice.

Can I become an ML engineer in 3 months?

Becoming a machine learning engineer in 3 months is highly ambitious but possible for those with a strong programming and math background. You can grasp fundamentals, complete projects, and gain entry-level skills, but mastering machine learning concepts and securing a job usually requires 6 months to 2 years of dedicated learning.

References:

Glassdoor. (2024, June 6) How much does a Machine Learning Engineer make? https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm
National Center for Education Statistics. (2024, May). Undergraduate Degree Fields. Annual Reports and Information Staff (Annual Reports). https://nces.ed.gov/programs/coe/indicator/cta
US Bureau of Labor Statistics. (2024, August 29). Computer and Information Research Scientists. Occupational Outlook Handbook. https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
US Bureau of Labor Statistics. (2024, August 29). Computer Programmers. Occupational Outlook Handbook. https://www.bls.gov/ooh/computer-and-information-technology/computer-programmers.htm
US Bureau of Labor Statistics. (2024, August 29). Data Scientists. Occupational Outlook Handbook. https://www.bls.gov/ooh/math/data-scientists.htm
US Bureau of Labor Statistics. (2024, August 29). Software Developers, Quality Assurance Analysts, and Testers. Occupational Outlook Handbook. https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm