Machine learning is a major subset of the rapidly growing field of artificial intelligence. You probably encounter the results of machine learning systems all the time, during such everyday activities as browsing Netflix or Youtube, using social media, making a purchase through Amazon, or even checking your email.

Read on to learn more about what’s involved in being a machine learning engineer, and what it takes to make a career as one.

In this article, we will discuss:

  • What does a machine learning engineer do?

  • Common backgrounds of machine learning engineers

  • Machine learning systems and skills

    • Does machine learning require coding?

    • Software engineering skills

    • Data science skills

  • How to become a machine learning engineer

What does a machine learning engineer do?

A machine learning engineer (or ML engineer) is the person who develops software and systems that can, on their own with no human intervention, make accurate predictions based on data and then begin learning on their own. While their precise role will change depending on the industry, organization or team, you can think of them as the ones who teach the machines to teach themselves.

To put it another way, machine learning engineers are the ones who make organizations become more efficient by harnessing the combined power of data and automation.

Working in collaboration with data researchers and scientists, software engineers and product managers, a machine learning engineer will analyze and interpret data sets, develop usable software based on that data, and create ways to incorporate machine learning models in production.

Machine learning systems are in high demand across a range of big data-dependent industries looking to become more efficient through automation, including auto, finance, health and entertainment. Of course, this means that those who can teach those systems to learn are in high demand too, with professionals typically attracting generous salaries.

Common backgrounds of machine learning engineers

Being a machine learning engineer is a multidisciplinary role, requiring the analytical skills of a data scientist and the technical development skills of a software engineer. The role will be most suited to someone with an education or professional experience in computer science, artificial intelligence, software development, statistics, data science, or data engineering.

That said, machine learning and artificial intelligence being such relatively new fields, there is plenty of room for newcomers who are willing to learn.

Machine learning systems and skills

Does machine learning require coding?

If you’re interested in a machine learning engineering role, coding is an unavoidable part of the job – building the code that powers the systems and programs is fundamental. A familiarity with programming languages such as Python, Java and C++ is typical.

Software engineering skills

A strong knowledge of and experience with software engineering is another necessary asset for the aspiring machine learning engineer. You can expect to have to know your way around such tools as TensorFlow, Spark and Hadoop, R Programming, Apache Kafka, Weka, and MATLAB. A good knowledge of natural language processing, machine learning libraries, neural networks, regression models, and informational retrieval will be useful too.

Data science skills

The work of the machine learning engineer has significant overlap with that of the data scientist. You will be expected to have a knowledge of and expertise with probability statistics, data modeling and data evaluation, as well as experience with machine learning algorithms and machine learning libraries.

How to become a machine learning engineer

Being familiar with the fundamentals of data science and software development will be essential to anyone wanting to break into the field of machine learning. How that knowledge and experience is built varies from person to person.

Some machine learning careers will specify the need for a bachelor’s degree in computer science, mathematics, statistics or similar fields, while many more will require a master’s degree or Ph.D in those subjects.

Self-study is not out of the question, but only knowing machine learning and deep learning concepts is not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of UCSD Extension's Machine Learning Bootcamp curriculum is focused on production engineering skills.

Ultimately, the essential qualification will be being able to assure a potential employer that you have the knowledge and experience to get the job done, and apply your theoretical knowledge to projects in the real world.

Whichever path you choose, trust that a career as a machine learning engineer is sure to involve plenty of work and serious commitment.

If you choose to pursue a career in machine learning engineering, you can be confident in the knowledge you’d be part of a new and exciting field that is growing and evolving every day, at the cutting edge of deep learning and artificial intelligence. The reliance on machine learning is only expected to increase in the coming years, as more companies discover more ways to make use of big data.

Find out if you’re eligible for UCSD Extension's Machine Learning Bootcamp. In this six-month bootcamp, you’ll learn linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.

You’ll put this knowledge into practice when you design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service, all with the support of a 1-on-1 industry mentor. Learn more about the Machine Learning Bootcamp mentor model.