How to Become a Machine Learning Engineer

• Machine learning is a fast-growing field.
• Machine learning engineers work with data analysts, software developers, and other engineers.
• Coding bootcamps and colleges train future machine learning engineers.
• Senior machine learning positions may require a college degree and work experience.

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.

What Does a Machine Learning Engineer Do?

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.

What Are Key Machine Learning Engineer Skills?

• Programming skills in Java, Python, Scala, and SQL
• Proficiency in machine learning algorithms
• Ability to deploy machine learning models
• Knowledge of software engineering best practices
• Experience in full-stack and end-to-end development
• Solid verbal and written communication skills
• Fluency in Amazon Web Services or other cloud platforms

Machine Learning Engineer vs. Data Scientist

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.

How to Become a Machine Learning Engineer

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.

Step 1: Learn How to Code

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.

Step 2: Choose a Machine Learning Educational Route

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.

Step 3: Get Hands-on Experience in Machine Learning

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.

Step 4: Get a Machine Learning Internship or Entry-Level Job

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.

Step 5: Keep Building Your Resume

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.

Step 6: Apply for Machine Learning Engineer Jobs

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.

How to Find Machine Learning Engineer Jobs

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.

What's the Average Machine Learning Engineer Salary?

Machine learning engineers made a median salary of $131,490 in May 2021, according to the Bureau of Labor Statistics (BLS). The bottom 10% of machine learning engineers earned a median of $74,210 per year, and the top 10% of professionals made more than $208,000. Besides work experience, pay also depends on your education level and location.

According to Payscale, bachelor's degree-holders in artificial intelligence make an average of $96,000 a year, while master's degree-holders make an annual average of $103,000 as of September 2022. And the BLS reports that machine learning engineers made the most in Oregon, Arizona, Texas, Massachusetts, and Washington in 2021 —where they respectively earned between $166,450-$136,770.

Frequently Asked Questions About Becoming a Machine Learning Engineer

What qualifications do I need to be a machine learning engineer?

Machine learning engineers typically need at least a bachelor's degree and certifications in machine learning. It's also good to have a few years of work experience in machine learning, software design, data engineering, or a related field.

That said, some employers care more about your experience than your education. You could get a machine learning engineer job after completing a bootcamp if you have a great resume and portfolio. However, other employers might require a master's degree.

How long does it take to become a machine learning engineer?

That depends on the educational route you take. Bootcamps can take a few months to complete, whereas degree programs can take upwards of six years for some students.

Your years of experience are also a determining factor. Some machine learning engineers need 1-5 years of experience before entering the role.

Is machine learning a good career?

Yes. Machine learning is a fast-growing industry that provides a high salary compared to the national average. If you enjoy coding, AI, and problem-solving, machine learning could be a great career choice for you.