Becoming a Machine Learning Engineer

According to Glassdoor, the average salary of a Machine Learning Engineer in the following cities is the following:

• San Francisco: $136,000/yr
• New York City: $118,000/yr
• Los Angeles: $101,000/yr
• Boston: $105,000/yr
• Washington D.C $100,000/yr

These metrics should come at no surprise. According to Linkedin's 2020 Emerging Jobs Report, the fastest-growing careers are related to Machine Learning and AI.

So you might wonder, who can become a Machine Learning Engineer?

In short - anyone can become a machine learning engineer! That said, perhaps the question you mean to ask is “how can I become a machine learning engineer?” Fortunately, there are a number of routes you can consider for becoming a machine learning engineer.

One of the most common paths - though by no means the only path - is to start off with a software engineering background. After building a solid foundation, the next step is to gain statistics and machine learning skills necessary in the field.

A more difficult path involves teaching yourself both the software fundamentals and the machine learning theory concurrently. It's doable, but tough.

Another, perhaps more efficient path to becoming a machine learning engineer, is to enroll in a comprehensive bootcamp program. Luckily, Springboard’s MLE program is here to help.

Who does Springboard accept into their MLE program?

Most students enter the Springboard MLE program with experience as data engineers, QA engineers, back-end engineers, software engineers, or other roles in application development. It’s important to note that students with at least one year of experience working in the software engineering industry have the highest success rate.

Outside of software engineering disciplines, professionals with experience in data science, computer science, electrical engineering, applied math, and other related fields tend to have great success transitioning into machine learning.

Since Springboard’s MLE program was built specifically to help those with software engineering backgrounds transition into machine learning, students coming from an adjacent or unrelated program can experience challenges. It is highly recommended that students enter the program with a strong engineering background and proficient knowledge of a modern programming language (i.e., C++, Java, Python).

Who Should Consider Springboard's MLE Career Track?

In our experience, students who succeed in Springboard's MLE Career Track are determined. They don't see Machine Learning as a hobby, but as their next step in their career. They enroll because they want a new job as a Machine Learning Engineer, or because they want to grow into a more challenging role within their current company.

When considering the training options for breaking into machine learning, many students decide to enroll in Springboard’s MLE career track because they are not interested in going back to school for two years or more to obtain a very similar outcome. Instead, the MLE Career Track offers a project-based learning experience which allows students to build and deploy ML models after learning the theory in class. The program comes with one-on-one mentorship and weekly phone calls with an AI expert in the field, plus career coaching to help graduates begin their machine learning career.

Wondering if you’d be accepted to Springboard's MLE Career Track?

Check Springboard's Machine Learning Engineering Career Track website, the application is free and only takes 10 minutes.

This post was sponsored by Springboard. To learn more about Springboard, visit Springboard.com or check out their reviews on SwitchUp.