With all the talk of artificial intelligence (ChatGPT anyone?), it may seem like becoming a machine learning engineer is the smart career move. And it might be if you’re willing to build up your skill set and have the patience to pursue an advanced education.
Ready to learn how to become a machine learning engineer and discover if it’s the right career for you? This guide covers it all!
What Is a Machine Learning Engineer?
A machine learning engineer trains artificial intelligence (AI). They work at the intersection of data science, statistics, mathematics, and coding to research, build, and refine the models that teach an AI how to function without human intervention. To make this happen, machine learning engineers collaborate with a larger team that often includes data scientists and programmers.
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How Much Does a Machine Learning Engineer Make?
Because machine learning is a fast-growing and evolving field, salary information is sparse. However, ZipRecruiter estimates that the national average salary for a machine learning engineer is just under $129,000 per year (or $62 an hour) based on location, duties, and experience level. Indeed estimates the average base salary for a machine learning engineer is approximately $161,000 per year.
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What Does a Machine Learning Engineer Do?
Much of a machine learning engineer’s time is spent designing, building, and maintaining the models that train the system. But machine learning engineers also perform statistical analysis, run experiments, and conduct tests to further improve the models. Additional duties include:
- Verifying data quality
- Training and retraining the system to improve results
- Selecting the proper data sets to create the training models
- Researching and implementing algorithms and tools
- Identifying which models to use as new algorithms
- Identifying and correcting distribution errors that impact how the model learns and performs
What Does a Typical Day as a Machine Learning Engineer Look Like?
According to Robert Weisgraeber, cofounder, managing director, and CTO of AX Semantics, in addition to spending much of the day on the “obvious computer science-related part,” you’ll also engage in a lot of data wrangling and business analysis.
Weisgraeber explains:
“Even if you have data, your data may not be in an ideal format. Many data sources need to be cleaned, tuned, reformatted, etc., in preparation for training or validating your actual model.
[Business analysis is] understanding the core of the business problem that needs to be solved. So, a lot of reading, analyzing, and understanding the background material — and this is very interesting because the topic will most likely come from a completely different domain. It could be linguistics, biology, business, or economics.”
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What Industries Do Machine Learning Engineers Work In?
Machine learning engineers generally work at technology or software companies that specialize in AI. Some companies may have a specific focus, like training AI to analyze a patient’s health records or creating an autonomous vehicle. Others have a broad specialty, like creating chatbots for e-commerce sites.
What Kinds of Jobs Can a Machine Learning Engineer Have?
When asked about the kinds of jobs a machine learning engineer could have, Weisgraeber says, “I don’t know yet. I’ll answer that in a few decades.”
This speaks to how new machine learning is! Many of the job titles and career paths you might consider may not exist yet. But you can still get an idea of how a career in machine learning could look.
“Tech in general has moved to a way of very flexible career paths where specialist careers are very valued and high-income jobs, so you can advance your career even if you don’t want to be a manager,” says Weisgraeber.
Machine Learning Job Titles
Because machine learning is a smaller part of the larger fields of data science and computer science, you may find some overlap in machine learning job titles. Below is a list of common job titles you may run into as you search for a role in machine learning:
- Machine learning engineer
- Artificial intelligence engineer
- Big data engineer
- Computer and information research science
- Data analyst
- Data engineer
- Data scientist
- Research scientist
- Applied research scientist
Pros and Cons of Working in Machine Learning
Though machine learning is a new and evolving field, like any career path, it has pros and cons. Weisgraeber says one of the things you may consider a con is, “how much time you spend with ‘simple’ Python scripts, aggregating and cleaning data, and how much hardcore statistics and stochastics is needed to set up scenarios that allow you to interpret results correctly.”
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But if you enjoy working with numbers and large data sets, he says the advantage of working in machine learning is, “It’s definitely a really future proof job, and you can be part of building the future.”
How to Become a Machine Learning Engineer
The path to becoming a machine learning engineer is long. Most people earn an undergraduate degree in computer science, statistics, or data science. After that, they might work at a technology company in an entry-level job, but it won’t be as a machine learning engineer. Instead, they’ll likely work as a programmer or data analyst.
Most machine learning jobs require at least a master’s degree in machine learning (or a related field), and some even require a Ph.D. You will also likely need several years of related experience.
In addition to your formal education, you can also pursue certifications or attend a bootcamp to practice and enhance your skills.
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That said, Weisgraeber says your skills and abilities will make a crucial difference in your job search. “A lot of employers in this area hire people for their personality and skill rather than your certifications. Your working side project could make you more interesting than your master’s thesis.”
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“I’d recommend that you start tinkering on your own time with some problems around you,” he continues. “You can (and should) learn a lot from online material. But you should also teach yourself to apply this to real-world problems and learn what you enjoy doing.”
What Skills Does a Machine Learning Engineer Need?
Machine learning engineers need exceptional data science and computing skills (like software development). They also need a deep understanding of math, statistics, and data visualization.
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In addition to these hard skills, machine learning engineers need strong soft skills. They spend a fair amount of time communicating and collaborating with team members and stakeholders.
You’ll also need exceptional abstract thinking abilities. According to Weisgraeber, machine learning engineers must have a conceptual understanding of how their models work. This requires you to think logically about the math and abstractly about how the model comes to a conclusion based on that math.
Finally, Weisgraeber points out that while this is an emerging field with new ideas, terms, and processes, it’s crucial to keep in mind all of the data and information that others have used.
“There’s a lot of hype out there, every single day. But don’t forget to always bring that into the context of what is known already: old research, old methods, old knowledge. All might still be true.”
Want to see what it’s like working in machine learning, AI, or data science? Enroll in one of our free data or software engineering virtual job simulations today!
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