More data has been created over the last 2 years than in the rest of human history combined. So it's no surprise that machine learning, a field that gives computer systems the ability to learn using this data, has grown in popularity. But what exactly does a machine learning engineer do?
A machine learning engineer performs very specialized programming in order to create code and systems that progressively improve as they run. In a sense, they create programs that "learn" as they go. The career is exciting, and this blog will cover what type of work machine learning engineers do, what their salary expectations are, and how you can train to work in this exciting field.
What does a machine learning engineer do?
In an interview with the Codecademy Team, data scientist Hillary Green-Lerman shares, “Machine Learning is about using the data you already have to make predictions." Machine learning engineers create systems so that computers can learn and make predictions on their own. This includes things like facial recognition software, recommendation engines, speech recognition, and more.
The types of projects you'll work on will differ depending on the type of company you join, so if this type of work interests you, search what’s out there to get a feel for the types of companies that offer positions. In general, a machine learning engineer will be expected to:
- Develop highly scalable code for multiple applications
- Maintain, create, or streamline data pipelines
- Build real-time machine learning applications for customization purposes
- Keep impeccable documentation
- Work within an Agile team
- Seek out opportunities to improve processes and systems across the tech stack
Machine learning engineers prepare and clean data, then select a model to use that data, which will provide a final result of recommendations based on identified patterns. As the program continues to work (for example, think of the Netflix algorithm recommending the next show to you), it’ll get better and better at delivering answers with more experience knowing the user.
In many roles, the machine learning engineer will need to also wear the hat of a data analyst, IT expert, or full-stack engineer. Job openings are advertised as machine learning engineering to attract the best and brightest, but in reality, a well-rounded programmer and problem-solver is what many companies are after.
Machine learning engineers also work hand-in-hand with data scientists, who create models that the machine learning engineers feed data into and then scale to production levels. They take the theoretical models from the scientists and put them to work on massive scales.
Depending on the tech stack used and the company’s actual product/service offerings, a machine learning engineer will spend their day using code to contribute to the company. They will create a program that’s trained on a set of data, and then improved upon as more information becomes available. They can do this in any number of ways, and for any number of applications, including:
- Personalized recommendations on streaming software, such as movie or music recommendations
- Recommendations for complementary products to what customers have purchased
- Image recognition software improvement
Machine learning engineer skills
A machine learning engineer needs a few general skills to perform in their job:
- Meticulousness in working with data and documentation
- Communication skills to interface with their team
- Creativity and an open mind for solving problems using code
- Big-picture thinking, understanding how a program can address a larger business problem
In addition, machine learning engineer skills must include some of the detailed, technical skills required for them to program and train models. Just like any other developer or engineering job, different jobs will require different sets of skills. But to give you an idea of the type of skills you’ll need to have, here are some examples:
- Python programming fundamentals
- Tensorflow programming fundamentals
- Proficiency in Recurrent Neural Networks (RNN)
- Data preparation processes
- Pretraining data methods
- Machine learning libraries and frameworks (e.g., pandas, keras, sci-kit learn)
Machine learning engineer salary
The average machine learning engineer salary is $114,579 in the United States. That number is expected to increase 13% by 2026, making machine learning engineers’ earning potential that much greater.
In addition, the demand for machine learning engineers is high. The U.S. Bureau of Labor Statistics predicts that all software developer (which machine learning engineers fall under) employment will increase by 22% over the next decade.
How to become a machine learning engineer
Back when the only way to learn was with a traditional college degree, most computer science and software engineers would get a four-year degree to qualify them for a job. But now, you have more options for learning the skills required to become any type of software engineer, machine learning specialists included.
If you're a beginner when it comes to machine learning, you can gain the skills you need through online courses. You can even earn certificates that you can put on your resume and LinkedIn profile to show you’ve trained in the right topics and languages.
Because machine learning engineers work very closely with data scientists, you might consider gaining some skills in this area as well. It’ll allow you to understand the models you’ll be working with better, and some jobs will require you to also wear the data scientist's hat. If you’re keen to work at a start-up, you’ll more than likely need to do a variety of jobs. But don’t worry, that’s the exciting part! You’ll be able to solve many problems from start to finish using both machine learning and data science skills.
Lastly, if you already have a good grasp on machine learning topics and you’ve worked on projects in the field, but you need to brush up on your use of Tensorflow, you can just take a single skill path on that subject alone. Many machine learning positions will require you to use Tensorflow, because it’s a great tool to make it easy to train, test, and tune learning models without getting into the weeds of complex math.
Beyond these basics, check out the exact company and job posting that you are going after to see what additional programming skills you might need. Just like with Tensorflow, you can always take a course on different languages or skills that might be required. And don’t stress. Many jobs will offer some form of training in their particular tech stack. So get the basic skills you need by learning in a way that suits you and on a timeline that doesn’t overwhelm you. The learning process should be enjoyable — as should be your next career as a machine learning engineer.