How to Become a Machine Learning Engineer: 5 Steps to Your New Career

Machine learning refers to the ability of computing systems to “think” like a human, and machine learning engineers work to make these systems accurate, useful, and highly detailed. Machine learning today is very complex: large sets of data are used to help build systems that make decisions and predict outcomes. Machine learning engineers are responsible for developing the algorithms that enable those systems to become more accurate over time without additional human input.

To put it into everyday context, think about anytime you ask Alexa or Siri a question and get an appropriate answer — that’s machine learning at work. These theories also enable Netflix to recommend movies you like and allow Amazon to show ads related to your previous purchases. There are endless possibilities for highly qualified machine learning engineers to build new technologies in nearly any industry.

In this article, we will discuss the machine learning engineer profession. We will also outline how you can start learning this skill — including through a data analytics bootcamp, which can help you jump-start your career in machine learning through hands-on, real-world learning.

What Does a Machine Learning Engineer Do?

We expect our technology to work intuitively and match our interests and intentions. Machine learning engineers are essential to achieving this, by creating systems that learn independently of human intervention and continuously improve upon their outputs. Typically, machine learning engineers work with artificial intelligence systems in an IT department.

Machine learning engineers sit at the intersection of data science and software design, and they usually need essential skills from both fields. Some of the more common machine learning engineer tasks include

Researching and designing machine learning systems

Studying and organizing data sets

Applying statistical analysis

Visualizing data

Analyzing and improving algorithms

Communicating with management teams to make better business decisions

Building artificial intelligence

Running machine learning experiments in different programming languages and with different libraries

Optimizing and scaling machine learning systems

Machine learning engineers also need to be collaborative, as they will likely work with disparate teams to analyze the needs of the business, create unique solutions, and define goals and objectives.

Machine Learning Engineer vs. Data Scientist

While machine learning engineers and data scientists work together, their roles and responsibilities complement rather than duplicate each other. Data scientists develop and test algorithms and have a deep understanding of the mathematical principles involved. A data scientist mines and organizes data, then interprets that data to determine the kind of machine learning applied. They pass this information on to machine learning engineers.

A machine learning engineer develops the software that puts these algorithms to work in a real-world situation. They deploy the ideas developed by the data scientists and interact with the products to refine the software and increase its efficiency.

Steps to Become a Machine Learning Engineer

Machine learning engineers come from all kinds of educational backgrounds, however, newcomers will likely need formal training to understand technical programs and languages. That said, many machine learning engineers have college degrees in computer science, artificial intelligence, or data science. Accelerated programs like data analytics bootcamps can also be useful for those looking to start a career in machine learning without a years-long commitment.

Once you have confirmed your interest in machine learning, follow these steps to becoming a machine learning engineer:

Learn the Proper Coding Languages

Because machine learning is about building software, an engineer will need to be very knowledgeable about computer coding languages and feel comfortable working with them in different environments. The coding languages you will likely need include

  • Java
  • C++
  • Python
  • SQL
  • C#
  • CUDA
  • R
  • Scala
  • Julia

An image highlighting the top coding languages.As a machine learning engineer, you will enable machines to mimic human thought patterns. Your programs will need to recognize patterns, find and use large data sets, and make predictions. Having an in-depth understanding of several coding languages is essential for solving different problems, as different systems often use different languages.

Learning to code is necessary for both front and back end machine learning developers. Our beginner’s guide to becoming a coder can help you determine the languages you need to learn for the position you want. Take advantage of some of the free resources to kickstart your learning.

Gain a Data Background With a Data Bootcamp

As a machine learning engineer, you’ll work closely with data scientists and will need a solid understanding of big data sets, how they are collected, and the best ways to analyze them. You will likely be required to collect, organize, interpret, and optimize data sets and apply your findings to software engineering. Learning more about data involves classes in statistics, databases, and programming languages.

You can learn these skills through a formal college degree program or a data analytics bootcamp. The Online Data Analysis and Visualization Boot Camp at Texas McCombs curriculum covers all of these topics while providing you with the opportunity to practice your skills in real-world environments. In just 24 weeks, you will be prepared to work with the technologies necessary to become an entry-level machine learning engineer.

Gain the skills you need to succeed with The Online Data Analysis and Visualization Boot Camp at Texas McCombs. Learn more and begin preparing for a new career today.

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Brush up on Math Skills

As a machine learning engineer, you’ll need a firm foundation in advanced math skills to succeed. All the algorithms used to create, understand, and optimize artificial intelligence utilize mathematical principles — and understanding these concepts can enable you to make smarter decisions about how to use the data and build systems that work efficiently.

Math courses recommended for machine learning engineers include

  • Linear algebra
  • Calculus
  • Statistics and probability
  • Discrete mathematics

Gain Experience With Machine Learning

Once you have mastered the basic concepts of machine learning, it’s time to put them into practice. Practice and experimental projects let you discover the practical uses of machine learning and give you a peek into the world of coding.

Start by recreating projects given in classes or that you find online — there are many resources available for those interested in growing their machine learning skill set. Once you’re comfortable with the basics, try building your own algorithm. Develop a project idea independently or find suggestions online or through classwork.

The Online Data Analysis and Visualization Boot Camp at Texas McCombs offers the opportunity to learn from seasoned professionals, experiment with new methods, and enter the field with confidence. You will also have the opportunity to work on projects independently and on teams to build a data analysis portfolio for use in future job interviews.

Find a Position and Continue Growing

Machine learning engineering was named the best job of 2019 because of its exponential growth and high salary. Many jobs are available, but they are highly desirable and can be very competitive as a result. Internships and entry-level positions will help you get the experience you need to continue growing your skills and your career options.

Most job posting sites feature listings from a wide variety of employers seeking engineers of varying skill levels. Craft a readable and comprehensive resume that highlights your skills and experience, and show off your projects with your portfolio.

Machine Learning Engineer Interview Questions

In the competitive field of machine learning, you need to be prepared to demonstrate your skills and knowledge during the interview process. Be able to answer some of these common machine learning interview questions when applying for a position:

  • How does an LSTM work?
  • How many weights are in a 2D convolutional layer?
  • What is the kernel trick in support vector machines?
  • What’s the trade-off between bias and variance?
  • Explain how an ROC curve works.
  • What is Bayes’ theorem? How is it useful in a machine learning context?
  • How do you handle missing or corrupted data in a data set?
  • How can we use your machine learning skills to generate revenue?

Not only do companies need to know that you have a firm grasp on machine learning, big data, and coding — they also want to see that you will be an asset to their business. Be able to discuss how your experience fits the goals of the company and show that your knowledge will improve their bottom line and increase customer retention.

Machine Learning Engineer Career Path

Machine learning engineer professionals have a wealth of roles and companies to choose from. And, as our technology leans on the ability to understand user needs and anticipate their desires, the need for machine learning engineers will continue to grow as well. Users expect their technology to work intuitively and provide accurate results. Machine learning engineers make that happen.

Technology continues to grow at an astounding pace and changes regularly, and many programs can quickly prepare you for a career in machine learning. Now is a great time to start your machine learning education with a bootcamp — here, you will learn crucial machine learning skills to enter the field or continue your education.

Machine Learning Engineer Job Growth

The demand for expert machine learning and artificial intelligence engineers has experienced explosive growth in recent years. Much of our new technology is dependent on machines learning our patterns, recognizing our speech, and anticipating our needs. The U.S. Bureau of Labor Statistics (BLS) projects a 22 percent job growth by 2030 in the broad field of computer science, which includes machine learning. That is much higher than the average job growth rate across all occupations.

Machine Learning Engineer Salary

The U.S. median salary for computer information research scientists, including machine learning engineers, is $126,830, according to the BLS. Machine learning is essential to software publishing, scientific research, the federal government, and higher education. Machine learning engineers who find employment in software publishing can expect to make about $145,920 per year, more than three times the national average salary for all occupations.

How to Become a Machine Learning Engineer FAQs

If you’re just starting out in your learning, you may have questions about what it means to be a machine learning engineer. While this may differ from company to company, there are some consistencies across all fields.

Machine learning engineers use large data sets to build programs that “think.” Over time, the program becomes more accurate and efficient as it learns patterns and anticipates needs. Many of us use these technologies daily: machine learning powers website chatbots, translation programs, voice recognition software, and much more.

A study from Robert Half found that 83 percent of managers in all fields either currently use or plan to use artificial intelligence or machine learning in the next five years.

An image highlighting that 83% of managers are either using or plan to use artificial intelligence or machine learning in the next five years.

Tech giants and small businesses alike remain interested in qualified machine learning engineers to help them become more efficient. Most entry-level computer research scientists have a master’s degree or higher, while others might work in computer programming with an associate or bachelor’s degree in mathematics, computer programming, or information technology.

Regardless of the educational path you choose, you will need to learn these skills to be a machine learning engineer:

  • Applied mathematics
  • Computer science fundamentals
  • Coding
  • Machine learning algorithms
  • Data modeling and evaluation
  • Neural networks
  • Natural language processing (NLP)

In addition, these soft skills are important when you are working with a team and communicating with leadership about projects and decision-making:

  • Communication
  • Analytical skills
  • Attention to detail
  • Logical thinking
  • Problem-solving

There are jobs available to machine learning engineers at all levels of education. If you’re eager to get started, a flexible 24-week bootcamp will prepare you with the skills for data visualization, programming, and software development. A data analytics bootcamp can also teach statistics, programming, and front end visualization with a combination of classroom learning and real-world experimentation.

Additionally, many machine learning engineers earn bachelor’s or master’s degrees in artificial intelligence, data science, science engineering, and other related fields.

Coding is a required skill for machine learning, as it is key in machine learning integration and data tracking. The machine learning engineer will need to know how to write code as well as troubleshoot and update code to make it more effective.

You will likely need to know JavaScript, the world’s most popular programming language. Due to the language’s ubiquitousness, there are many websites, books, bootcamps, and self-paced projects available that will help you learn JavaScript quickly.

Some of the additional coding languages you should be familiar with are:

  • Python
  • C++
  • R
  • Lisp
  • Prolog
  • HTML
  • Java
  • Julia
  • GO
  • TypeScript
  • Scala

Machine learning is a growing field that will hire many professionals in the coming years. If you’re interested in making the leap into this field, you’ll need a solid educational background in math and computer science to complete the daily tasks of a machine learning engineer. A great way to start your education in machine learning is to attend a data analytics bootcamp. These short courses allow you to quickly master the foundational knowledge of machine learning.

Other skills that will help you stand out in the marketplace when applying for machine learning jobs are

  1. Curiosity and the ability to stay with a problem
  2. Being a team player
  3. Translating business problems into mathematical statements and finding ways to solve those problems
  4. Learning programming languages and their libraries
  5. Tailoring your knowledge to the specific field you work in

Machine learning is a type of artificial intelligence that mimics human thought. Through experience and feedback, machines get better and faster at solving problems and predicting outcomes.

Today’s websites and algorithms capture large quantities of data. Machine learning offers an efficient way to collect, store, analyze, and use that data. This information can help organizations and businesses make better decisions and produce intuitive, useful products.

Machine learning enables a website to recommend a product you might like based on past purchases or views. It’s also saving lives by detecting disease through apps, saving money by catching signs of fraud, and creating new means of transportation with autonomously driving cars. Artificial intelligence helps manufacturers reduce costs and losses and anticipate market needs. Health care, transportation, retail, finance, and nearly every other industry benefit from machine learning. In fact, most industries are currently using machine learning or plan to do so soon.

Begin Your Machine Learning Career Today

For anyone interested in computer programming and artificial intelligence, a career in machine learning is a great choice. These skills are currently in demand and will continue to be as technology develops and evolves. Keep in mind, a machine learning engineer should be comfortable with math and statistics and understand computer languages and their libraries used to create new programs.

If you’re interested in pursuing one of these in-demand roles sooner than later, try advancing your skills through a bootcamp program. In just 24 weeks, you will learn about data analytics and machine learning. The Data Analysis and Visualization Boot Camp at Texas McCombs is a flexible and comprehensive program that will give you the skills to land your first job or enhance your career in the machine learning field.

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