Machine Learning Jobs – A Complete Guide

Half the time you see the term artificial intelligence today, you will find machine learning somewhere in the same sentence. In fact, most media coverage treats the two fields as the same thing.

That’s certainly not the case when it comes to career paths, however. Machine learning comes with its own degree and employment options. Some of those lead to AI careers. Others can take you on completely different paths into other roles entirely.

Machine learning career paths offer some slightly different options from the standard route into AI jobs.

That’s because machine learning, though a critical component of artificial intelligence today, is one of the most mature techniques in the AI toolkit. It’s been in use in a wide range of applications that are out of the AI mainstream and not directly connected with what people think of as AI jobs:

So machine learning jobs exist both within and as a part of the artificial intelligence industry, and in many industries that aren’t necessarily concerned with the overall pursuit of AI. Machine learning experts have the skills and ability to jump back and forth, opening up way more careers than a stand-alone degree in AI might unlock.

Machine Learning Jobs Can Lead to Any of the Three Common Career Paths in AI

If you’re looking at a website called AIDegreeGuide.com, though, you’re likely most interested in the artificial intelligence careers that machine learning can open up. So it’s worth taking a quick look at the three common kinds of jobs, and degrees, that are unfolding in the AI world.

The first is pure computer science studies and investigations into AI. Powered by machine learning algorithms and statistical techniques, research scientists and programmers in this area are uncovering new approaches and optimizing generative models that will power the next generation of AI tools. They tend to be most concerned with theoretical approaches and must be heavily grounded in ML specialties like deep learning and neural networks.

The second is the business applications and uses of AI. These jobs require a broader education. They involve taking AI theory and turning it into practical tools and systems that can solve real-world business or consumer needs. ML here is valuable for its proven track record in business applications and data science.

The third are highly specialized roles in specific technical fields where AI and ML systems can drive new possibilities in significant and critical applications: think self-driving cars, or medical diagnostics. These positions require an education that includes both strong ML training and specialist subject-matter expertise.

Science and Math Skills Are a Key Part of Preparing for Machine Learning Careers

The degrees you can earn to unlock any of these paths are segmented in the world of AI. In machine learning, however, degrees tend to revolve around ML itself. Certain schools might offer programs that build skills for specific applications in the field:

Those, in turn, can line up with one of the three general AI career paths. Autonomous systems and neural computation, for instance, are likely to fall into jobs that fit the specialist technical roles in AI. Data science experts may find a natural fit in business applications, where big data already plays a significant role.

Before machine learning became common, many experts developed their skills through degrees in statistics, math, physics, and computer science—a path that is still open today.

But machine learning is really a specialization all its own, one that has applications in all three of those areas and more. Degree programs in ML can be tailored to specific career paths through wise selection of elective coursework or graduate capstone projects.

Since ML programs are often offered as an emphasis on other majors, the direction they will take you may be clear just from the title. A Bachelor of Science in Computer Science Machine Learning specialty, for instance, is going to come at ML from the computer science side of the table. A Master of Science in Machine Learning and Data Science is going to take you firmly into the analytics end of the industry.

But every machine learning degree or concentration will stock your knowledge base with core coursework in:

High-level mathematics and statistical techniques
Basic Python and Java programming
Database structure and administration
Data mining and predictive analytics
Deep learning and algorithm design
AI Ethics and Society

And most let you branch out through electives in related areas like:

Different Degree Levels Pave the Way to Different Responsibilities in Machine Learning Jobs

As a more mature field than artificial intelligence overall, machine learning offers more opportunities at more varied educational degree levels.

AI in general is still very much in the exploratory phase. Most jobs in that industry require high levels of education to support basic research or advanced systems development. You don’t get far without a graduate degree behind you; the most elite positions are reserved for PhD holders.

Data science is a well-established field that has gone well past the point of exploration and breakthroughs. Although a lot of exciting new developments happen all the time, there are also plenty of roles available for people who are operating, as well as developing, the technology. That takes less advanced education, so a four-year Bachelor of Science in Machine Learning is perfectly adequate for entry-level jobs.

Two-year associate programs, like an Associate in Engineering Technology with a Specialization in Machine Learning and Design Techniques, are quite rare. They really only offer an on-ramp to a bachelor’s program rather than serving as job qualifications themselves.

Similarly, however, a bachelor’s degree is good and necessary preparation for a more advanced program like a Master of Science in Machine Learning, or even a PhD in Machine Learning. Taking between two and five years to complete, such graduate programs come with more research and original investigation into advanced ML techniques. They prepare graduates for the most advanced positions in the field.

You’ll also find plenty of Certificate in Machine Learning programs available today. These are short, usually less than a year, and relatively inexpensive. They put you through only a handful of classes, each focused on the essence of ML skills. They offer a path for existing professionals with strong coding, math, or statistical backgrounds to quickly master ML for new opportunities in AI or related fields.

Because they are so short and intensive, you’ll also find certs that are focused on particular aspects of ML, like a Professional Certificate in Machine Learning and Artificial Intelligence, a Certificate in Machine Learning and Deep Learning, or a Post-Graduate Certificate in Data Science and Machine Learning.

Machine Learning Engineer Jobs Come with a Variety of Different Titles

The skills you bring out of those ML degree programs qualify you for positions in a very particular type of role. For the most part, that’s some sort of variation on the position of Machine Learning Engineer. You will see the job pop up with a variety of titles in job listings:

Machine Learning Research Engineer
Growth Machine Learning Engineer
ML Engineer for LLM Infrastructure
Engineering Manager, Machine Learning Workflows
Transportation Engineer Modeler
Machine Learning Performance Engineer
Foundation Models Architect

In other cases, ML credentials can stack with your other qualifications to fill more general positions. Plenty of ads for Software Engineers, Data Scientists, and Computer Vision engineers specify ML expertise.

Naturally, you will also find that knowledge can qualify you for other kinds of roles that are becoming more common in artificial intelligence engineering as well. An ML degree can unlock exciting new jobs like:

As more and more applications emerge for AI, you can expect to find ML expertise taking on more importance in other fields. Everything from financial analysis to content production may rely on ML as an essential technology soon.

The Industries Offering Machine Learning Jobs in AI and Beyond

Since artificial intelligence jobs are beginning to bloom in almost every industry imaginable, machine learning positions will follow. But machine learning itself has already been around for a while. In many cases, it has a strong penetration and independent role in industries entirely apart from their AI needs.

In some cases, machine learning roles align neatly with the three big areas of demand for artificial intelligence professionals. In other cases, they may cross over, or fit into other fields entirely.

The jobs, industries, and salaries for artificial intelligence careers are all covered in our AI career guide.

Machine learning, though, comes with a kind of subset of those positions. In AI, ML experts today are focused on the development and optimization of deep neural networks as the success story powering everything from ChatGPT to self-driving vehicles.

Those breakthroughs have come courtesy of ML experts with computer science backgrounds. Working in highly theoretical R&D positions, they have developed both algorithms and training techniques that are used to power generative AI.

On the business world path, ML engineers are working to turn their expertise into practical tools that will fulfill the promise of AI in industry and government. These can include:

Finally, ML expertise is playing a big role in the highly technical specializations where AI will create big changes—but only with enormous caution and accountability. Self-driving vehicles, expert systems that evaluate medical records and MRIs, and face recognition systems for national security applications may fall into this category. The downsides of getting it wrong are enormous. But the upside is tremendous for the organizations that get it right. They operate in industries like:

The Day-To-Day Experience of Machine Learning Engineer Jobs Resembles Most Information Technology Professions

The kind of work performed in these roles from day-to-day and industry-to-industry will vary widely. A computer vision engineer may spend much of their day honing training datasets and working with supervised machine learning systems to get an algorithm to identify tumors on chest x-rays from hints too subtle for radiologists to spot. An AI inference engineer on a self-driving vehicle team may work on integrating sensor data from half a dozen technologies and figure out ways to feed it all into a single neural network for enhanced results.

While they exist in a wide range of industries and deal with a stupendous number of different goals and responsibilities, what do machine learning jobs have in common?

You can count on them all being desk jobs, for one thing. Expect to spend a lot of time in front of multiple computer monitors. You’ll be doing a lot of research to keep current with the latest developments in a fast-moving field.

Higher degree levels will typically lead to ML jobs with more responsibilities and more opportunities for development and original research.

Math figures into machine learning positions heavily. The technology makes use of statistics to power self-learning algorithms, so basic computation and mathematic problem-solving will be a daily activity.

Putting those solutions into play means generating computer code in languages like R, Python, or C++, so you’ll need to keep your fingers limber and your brain in analytical mode. Since most ML solutions today rely on extremely large volumes of data, you can expect to be reviewing and managing databases and sensor data streams frequently, as well.

All of this comes with practical requirements to coordinate your efforts with those of other engineers, senior leadership, and sometimes end users. So you’ll have to meet and communicate regularly as part of your job duties.

High Salary Levels Are Common in Machine Learning Engineering Jobs

ML jobs in both AI and other areas that rely on the technique have one other thing in common: they all come with solid paychecks.

Naturally, what you earn will revolve around your level of education and experience in machine learning. Similarly, you are going to be impacted by industry trends and profits. Don’t expect to make as much working in low-margin retail ML engineering positions as you might as a medical services ML engineer.

Although machine learning has been around for a while, it hasn’t been categorized by the Bureau of Labor Statistics (BLS) into its own unique job role. Instead, like AI engineers in general, they are filtered into any one of a half dozen or so common industry roles under which the agency keeps data.

Those include such positions as:

Since ML is such an in-demand area of expertise, it’s a solid assumption that ML engineers working under those roles fall into the upper ranges of salary levels. These represent the top ten percent of such positions for 2022.

Since BLS does track data by industry, it’s possible to ferret out a little more information about potential pay by drilling down industry by industry. Some of the common BLS-designated industries where ML engineers work include:

Computer Science

- Computer and Information Research Scientists – $156,870
- Computer Programmers – $100,780
- Software Developers, Quality Assurance Analysts, and Testers – $115,820
- Data Scientists – $117,800

- Computer and Information Research Scientists – $201,300
- Computer Programmers – $108,580
- Software Developers, Quality Assurance Analysts, and Testers – $135,470
- Data Scientists – $131,840

Business and Government

- Computer and Information Research Scientists – $118,020
- Computer Programmers – $107,500
- Software Developers, Quality Assurance Analysts, and Testers – $124,880
- Data Scientists – $117,720

- Computer and Information Research Scientists – $115,330
- Computer Programmers – $91,840
- Software Developers, Quality Assurance Analysts, and Testers – $89,020
- Data Scientists – $82,840

- Computer Programmers – $95,940
- Software Developers, Quality Assurance Analysts, and Testers – $107,950
- Data Scientists – $85,180

- Computer and Information Research Scientists – $176.920
- Computer Programmers – $104,640
- Data Scientists – $117,110

Professional Roles

- Data Scientists – $91,770

- Computer and Information Research Scientists – $141,230
- Data Scientists – $92,970

Physical, Engineering, and Life Sciences
- Computer and Information Research Scientists – $162,400
- Data Scientists – $126,280

Not every role in each industry has enough presence to warrant being included in BLS data. Also, BLS lists the average salary rate, not those in the top tenth percentile, at this level of breakdown. So in many cases, you’ll have to imagine positions in ML engineering pay even better than what is listed. For master’s and PhD qualified ML engineers, it will be higher yet.

Machine learning careers today really open up the best of all worlds to those with the right qualifications. With strong and proven demand in different tech fields across industries, it’s a viable career path in its own right. And as a key piece of the latest developments in the overall progress of artificial intelligence, it’s also a path that has unlimited upside in that industry.

2022 US Bureau of Labor Statistics salary and employment figures for Computer and Information Research Scientists, Data Scientists, Computer Programmers, and Software Developers, Quality Assurance Analysts, and Testers reflect national data, not school-specific information. Conditions in your area may vary. Data accessed December 2023.