How to Become a Machine Learning Engineer

Machine learning engineers use artificial intelligence to help their companies solve problems. Whether it’s creating fraud detection or speech recognition software, machine learning engineers’ projects are crucial to businesses’ success.

Read on to learn how to become a machine learning engineer.

Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.

Becoming a machine learning engineer looks different for each person. The level of education you need depends on your career objective. Each employer and machine learning role has different requirements.

To find out if a career as a machine learning engineer suits your skillset, learn to code at a coding bootcamp or in college. Since machine learning algorithms run on code, aspiring machine learning engineers must learn to code. Although, the job requires more complex mastery of the functions that make artificial intelligence work. And machine learning libraries can make coding easy.

ML engineers often need to be proficient in object-oriented programming languages such as Python or Java.

Coding bootcamps offer machine learning education that takes weeks or months. But employers may prefer candidates who hold a bachelor’s degree in computer science or an advanced degree.

If you already hold a bachelor’s degree, a machine learning bootcamp can enhance your skills.

Jobs exist at all levels of machine learning. The educational requirements depend on your career goals. Work experience and fluency in Python may suffice for an entry-level job, but a senior role may require a college degree in computer science, statistics, mathematics, or physics.

Employers require machine learning engineers to have work experience and a portfolio. To get hands-on experience, you can take machine learning classes and ask instructors about research opportunities.

Once you know how to code in machine learning, get familiar with common algorithms, such as linear regression, Naive Bayes, Random Forest, and logistic regression. From there, you can learn to build machine learning models.

In addition, online communities such as Kaggle and Reddit’s r/learnmachinelearning help budding machine learning engineers get answers to their questions and connect with mentors.

During or after a coding bootcamp or college, you can apply for machine learning jobs or internships. Entry-level machine learning engineers work on engineering and research teams to use machine learning models and create applicable products.

Machine learning interns also work with machine learning engineers to create AI programs.

Machine learning continues to transform all industries. To give yourself the best shot at a machine learning engineer job, continue your education through higher degrees or certifications.

Colleges such as Massachusetts Institute of Technology and the University of California, Berkeley offer professional development programs. These programs train students in machine learning fundamentals such as linear and multiple regression, decision trees, and clustering and principal component analysis.

After gaining an education —either a formal college degree or coding bootcamp certificate — you can begin applying for jobs. How long it takes you as an aspiring machine learning engineer to land a job depends on your resume and portfolio. Your location’s demand for workers will also probably come into play.

Machine learning (ML) engineers use data to design applications and systems that address business problems. Programming languages such as Java, Python, and C++ help machine learning engineers perform their jobs. Specific projects vary by industry.

ML engineers use models and learning algorithms to complete tasks and make predictions. Much of the job often relies on data cleansing and sourcing.

ML engineers are data masters who use algorithms to automate processes. Data scientists are analysts with a deep understanding of the mathematics needed to create predictive models.

Machine learning engineers — who have developer backgrounds — design machine learning algorithms to create products. Data scientists must investigate and analyze algorithms needed for business solutions. In their jobs, data scientists focus on analytics.

The top tech companies — Amazon AWS, Google, IBM Corporation, and TIBCO —offer many computing resources for machine learning engineers. However, the competition to get a machine learning job, especially at a mature company, can be challenging.

Securing a machine learning position requires hands-on experience in system design and data structures, a robust portfolio, and a willingness to improve your skills. If you meet these qualifications, you can narrow your career search by making a list of your top industries and companies. After narrowing your search, seek out job referrals to get your cover letter and resume seen.

Machine learning engineers made a median salary of $145,080 in May 2023, according to the Bureau of Labor Statistics (BLS). The bottom 10% of machine learning engineers earned a median of $81,450 per year, and the top 10% of professionals made more than $233,110. Besides work experience, pay also depends on your education level and location.

BLS reports that machine learning engineers made the most in California, Washington State, Virginia, Maryland, and Texas in 2023 — where they respectively earned between $139,340-$202,910.

According to Payscale, bachelor’s degree-holders in artificial intelligence make an average of $93,000 a year as of July 2024, while master’s degree-holders make an annual average of $102,000.