How to Become an AI Engineer

In between the truly wondrous advances that AI researchers and scientists have made and the real-world uses that thinking machines might fill, there’s one key job that needs to be filled: Artificial intelligence engineer.

AI engineers pull the practical from the magic of the AI world.

They have the in-depth understanding of key systems and components to see how the whole machine comes together. But they also can turn those theoretical plans and ideas into functional tools that are used by real people.

In fact, behind the scenes AI engineers are already turning promises into performance:

Self-driving vehicles powered by AI decision engines and computer vision systems are navigating remote highways and dense cities almost entirely autonomously
AI-driven coding assistance tools are making a splash in the world of programming, taking common challenges and resolving them or optimizing code to run better
AutoML (Automated Machine Learning) powered by generative AI is allowing executives and staff to generate their own deep dives into big data by using natural language requests to formulate new machine learning algorithms

But that’s just the tip of the iceberg. For anyone who wants a hands-on role in the hottest technology job of the century, learning how to become an AI engineer will be right at the top of their list.

AI Engineering Joins a Long and Respected Lineage of Jobs That Put Technology to Practical Use

AI engineering is a brand new profession. Until a few years ago, no one had even heard of it. So it’s a field that is still in the process of being created.

But it has a long heritage to draw on, both in artificial intelligence development and in engineering more generally.

All Scots are engineers, and all engineers are Scots.

~ noted engineer and accomplished AI user Lt. Commander Montgomery Scott

You don’t have to be Scottish to become an AI engineer, but it can’t hurt.

That’s because practicality is your guide star in engineering careers. Engineers are about results. They learn and understand underlying theories, but putting those theories to use is the heart of the profession.

But your first task will be understanding how the field itself is starting to develop into unique career tracks and educational options.

1. Understand the Demands of AI Engineering Careers

Before you get started, it’s important to be clear on just what you’re getting into. AI engineering isn’t just a tough career path. It’s a career path that’s made of tough career paths.

An engineering career is tough. Programming is tough. Data science and statistics are tough.

Artificial intelligence engineering leaves them all in the dust.

This is a field where doctorates are common stuff and a master’s degree is often the price of admission. You don’t need the skills from just one of those fields named above; you need expertise in all of them.

That means developing world-class competency in foundational tools like:

That’s just to get started in AI engineering. On top of those bases you’ll need to build advanced capabilities in:

That’s before you can even think of starting to specialize in an area like Natural Language Processing or Computer Vision. Each of those comes with a unique area of mastery for AI engineers, and you’ll likely end up in one of those before your journey is over.

It’s a formidable 8 steps of education, accumulating experience, and developing expertise. But they are steps you’ll have to follow to become an artificial intelligence engineer.

2. Pick One of the Three Tracks in Artificial Intelligence Today

In part, the AI engineer career path is influenced by which of the three different branches of the artificial intelligence industry that are forming today.

These aren’t hard and fast fields yet, and few people in the industry formally recognize them. But as you look at both jobs and educational options, you’ll find that they are tending to cluster in these groups:

Dedicated research and development of advanced AI technology. These computer science-oriented roles are engaged in basic, foundational research. They are pushing the state-of-the-art forward through experimentation and new algorithm development. The roles in this area are usually found in think-tanks, big industry players, and academic institutions.
Applied uses of artificial intelligence tools in business and government. This is the area of AI where most people are first coming into contact with AI tools. As various industries are seeing the potential trickling down from the R&D world, they are hiring AI professionals to come up with practical implementations to solve real-world problems.
Expert applications of artificial intelligence in specific professional fields. These are fields where a high level of knowledge and expertise are required in both AI and in complex and sensitive professional areas. They stand to benefit from AI tools, but require more intensive integration and testing in critical fields like healthcare, the law, or high finance.

You’ll find jobs for AI engineers along all three of those paths. But because of the applied nature of engineering as a profession, they are likely to be more concentrated in the second two tracks.

You’ll also notice that those tracks influence everything from AI engineering degree curriculum plans to job requirements. While it’s a fluid enough industry that you can shift between those paths, you’ll find your way much easier if you align your goals and your educational plans before you even get started.

Some Industries Align More Closely with Particular AI Tracks Than Others

Industry isn’t necessarily the final word on which track a particular engineering job is on. There will be many positions in every industry that are on the general business applications track, for example. Every company has to handle things like paperwork and customer support, even if they are building advanced surgical robots in the back room. So you can land an AI engineering position leaning either direction at such companies.

On the whole, though, you’ll notice the most alignment between these tracks and industries:

Big tech companies
AI startups
Robotics companies

Business-to-business service providers
Software manufacturers and vendors
Outsourcing companies and staffing solutions firms
Consumer products companies

Healthcare informatics
Pharmaceutical research and development
Legal services
Transportation and automotive companies
Financial services and accounting firms

Because of the way AI is bubbling up from underneath the computer and data science industries, you’ll also find that a lot of jobs in AI engineering don’t necessarily say AI engineer right in the title. There are many positions that are taking on skills in the field gradually. In time, AI engineering will be such a core piece of the skillset for these positions that you can just assume you’ll need it.

Until then, whether the title reads Principal Software Engineer or Machine Learning Operations Engineer, be sure by checking the actual job description.

3. Earn a Bachelor’s Degree to Set Your Foundations

Everything in AI begins and ends with education.

For AI engineers, this is a long haul that may even start before you enter college. Artificial intelligence and machine learning, the subsidiary technology behind most big AI breakthroughs today, are rooted in higher mathematics and statistics.

Your earliest college classes, then, need to lay the foundations for the advanced maths you will eventually have to get into. And you’ll have an easier time getting into those essential courses if you’ve taken the more advanced tracks available at the high school level, up through trigonometry and calculus.

Without the right foundations, even the most advanced degree in artificial intelligence won’t help you become an AI engineer.

You’ll also need to build skills in data analysis and statistics. This often includes basic concepts like database design and management, SQL, data types, scrubbing, munging, and more.

Finally, this is the stage to start building elementary skills in programming. You will pick one more language such as Python, R, or C++ to start learning. You’ll also need an introduction to programming concepts.

The majors that will get you to this basic level of foundational skills are many:

Bachelor of Science in Computer Science
Bachelor of Science in Data Science
Bachelor of Science in Machine Learning
Bachelor of Science in Artificial Intelligence and Machine Learning
Bachelor of Science in Statistics in Computational Statistics

What’s important is that you line up your undergraduate education with the eventual requirements you’ll need to meet to get into a graduate program in AI or ML.

Is It Possible to Become an AI Engineer Without a Master’s Degree?

There are many positions in computer science, data science, or other kinds of engineering that don’t come close to requiring all the rigors of a graduate education. So you are probably wondering if you can stop at the bachelor’s level and go on to a perfectly productive career in AI engineering.

That day is coming. But right now, you’re running a big risk if you’re not advancing your education.

The demand in the field is such that you can find some artificial intelligence engineer jobs that are available for bachelor’s graduates. But it’s common that you will also need extensive experience to qualify for those jobs.

You also aren’t going to be at the top of anyone’s list for interviews. The level of specialized expertise that comes along with a master’s is something you can get through experience and diligent study. But most employers aren’t going to roll the dice if they have a better alternative.

As more and more master’s-prepared graduates hit that market, that’s going to make anyone without an advanced degree less and less attractive. On the other hand, as some of the hard problems are solved and AI becomes used more and more widely, there are also likely to be more intermediate positions that become available.

4. Move up to Graduate Studies to Hone Your Expertise

Although there’s a time in the not-too-distant future where you may not actually have to go all the way to the master’s level or beyond for AI engineering jobs, the reality today is that these are where the heat is in the profession today. According to Burtch Works, a professional recruiting firm working in the computer science and engineering space, more than half of AI professionals working today hold a master’s degree.

While a master’s degree is enough to get your AI engineering career going, it’s not necessarily the end of the educational road. There are still plenty of top jobs in the field that demand a doctoral degree, instead.

This is particularly true on that first track, the R&D path where the big breakthroughs and newest AI techniques are being pioneered.

Burtch Works notes that one third of AI professionals today have PhDs.

You can opt for either a master’s or a doctoral program as your goal. The master’s will take an additional two years past your undergrad studies; a doctorate between five and seven years (depending on whether you picked up a master’s first; in many PhD programs, earning a master’s is integrated into your studies if you haven’t already done so).

In either case, this is where you really start getting serious about AI and ML concepts and practices. Gone are the broad strokes of foundational math and coding. Here’s where you put the knowledge to the test.

That comes with advanced courses in:

They are all about taking the core skills you absorbed as an undergraduate and putting them to use in the actual creation of AI tools.

Getting Your AI Engineering Qualifications with an Educational Certificate

Although showing up with only a bachelor’s degree isn’t going to put you in the running for top AI engineering jobs, it’s also true that not everyone can go back and earn a graduate degree.

Particularly if you have a lot of experience in data science, math, statistics, or computer engineering, an AI Graduate Certificate might be a good option instead.

Certificates offer a smaller and more focused set of courses than a full graduate degree. They are often delivered entirely online. They are more affordable and take considerably less time to complete.

If you have a strong command of core programming and data skills, plus extensive experience in real-world engineering tasks, the familiarity you get with a certificate may be enough to shift to AI engineering without breaking stride.

There’s also room for electives at the graduate level. You can shape your training through classes in robotics, GPU programming, computer vision, natural language processing, evolutionary intelligence, and any other topic in the AI world.

Just like at the bachelor’s level, you will find a variety of degree programs that satisfy these course requirements. They include:

Master of Science in Computer Science - AI Specialization
Master of Science in Electrical Engineering, concentration in Machine Learning and Intelligent Systems
Master of Science in Artificial Intelligence
Master of Science in Data Science - AI Specialization
Master of Engineering in Artificial Intelligence
Master of Applied Science in Artificial Intelligence

Naturally, concentrations or specializations in your graduate degree studies will push your training on a path to a particular skillset or industry in AI engineering.

All of them will also require a capstone or thesis research project. Like electives, you get to pick your topic and shape your studies around it. At the doctoral level, in fact, the investigation into your dissertation topic is very nearly the entirety of your program. This gives you a taste of the research and experimental programs going on in the field. It’s a vital culminating part of your education, one that most employers will look at first thing.

Your Choice of School Can Be Key in Launching an AI Engineering Career

The schools you choose for your degrees are going to be important for several reasons. Clearly the availability of the kinds of coursework and concentration areas you are looking at is a big one.

Closely related are ties to industries where you intend to pursue your career. That’s your opportunity to make connections and learn the inside scoops on how AI is currently being done.

Quality is another factor that can’t be overlooked. There are clear winners in the AI educational race today. You can count the research paper citations, outside funding, and number of graduates going to work for big players like OpenAI, Google, Meta, and Amazon. So it’s always important to line up your applications with the places where professors are at the cutting edge of AI research and have the resources to bring you along.

5. Build Out Your Real-World Experience with AI Projects

Even in a hot field like AI engineering, you’ll face the perennial problem of jobseekers in every high-tech field: every employer wants you to have experience, but few of them want to hire you until you get it.

So anything you can do to put your new skills into practice without a formal engineering job will be a plus.

Fortunately, there are many time-tested ways in the tech industry in general to get that kind of experience.

Open-Source Project Volunteers

Some of the most important tools in AI engineering today are the result of open source software projects. TensorFlow, PyTorch, HuggingFace Transformers… all are active OSS (Open Source Software) projects. More are spun up all the time. Cutting your teeth contributing to one of these shows employers real, working code you have helped develop, and expands your familiarity with real-world AI engineering.

College Research Projects

One of the biggest benefits of an advanced degree is the opportunity to participate in research projects at your university. Even limited participation in the latest brainstorm of a well-respected professor is a big talking point in your early interviews.

Independent Projects

Of course, you can also forge your own path in AI software by pushing ahead with your own ideas on independent coding projects. Although some of the most advanced AI takes millions of dollars and months to train, picking a limited, but innovative idea to pursue on your own shows initiative and gets valuable experience under your belt.

Internships

Particularly for doctoral candidates, internships at big AI companies are practically a rite of passage. These help you ease into the industry, make contacts, and give you relevant experience to build on your education.

Network Broadly to Build Contacts to Support Your AI Engineering Ambitions

Making contacts is still a particularly valuable part of becoming an AI engineer. The industry is relatively small at this point, and you’ll see a lot of the same names skipping around between the major players.

Getting connected with your peers and finding mentors among current AI engineers can make all the difference in getting the opportunities you are looking for.

Like gaining practical experience, this will usually start in college. Your professors and fellow students all represent potential professional contacts. You also often have the chance to attend conferences, participate in Q&A panels, and attend presentations by other active AI engineers.

Your college career services team may also have suggestions or opportunities to help you expand your professional AI network while you are in school.

After graduation, you have to put more effort into outreach. Participating in OSS projects or being active on discussion boards like Stack Overflow can connect you with other AI engineers and programmers. Attending conferences or other events like competitions is also a good way to spread your wings.

Different areas of AI engineering have different opportunities, from the International Aerial Robotics Competition to the DARPA AI Cyber Challenge. Finding a relevant competition or conference is a quick way to get hooked up with people in your field who are good to know.

6. Earn a Relevant Certification in AI or ML

Professional certification has long been an important part of technology careers. AI engineering is shaping up to be no different.

Unlike the educational certificates offered by colleges, these certs are all about validation. While you can learn a lot in a college certificate course, certification is designed to prove that you have mastered certain skills or subjects. Offered by independent industry organizations or major technology vendors, professional certification is a third-party stamp of approval that employers have learned to rely on.

Because AI is still such a new field, the certifications that are most important or most widely respected really haven’t been nailed down yet. You can see an exhaustive discussion on our AI certification page, together with a list of the most promising options so far.

AI certs aren’t your only option. Since AI engineers are in the business of getting their hands dirty with the internals of building algorithms and deep neural networks, a machine learning certification might also be useful.

7. Land a Job as an Artificial Intelligence Engineer

This is the step where you put it all together and land the engineering job of your dreams working with artificial intelligence.

Demand for qualified AI engineers is strong—both software developers and hardware engineers, two of the closest matches for AI engineering in current Bureau of Labor Statistics categories, show faster than average growth rates. In fact, over the next decade, software developers are expected to see a 25 percent jump in available positions.

So you shouldn’t have trouble getting an interview. But landing the perfect position will require showing off all the education, experience, and expertise that you have developed along the way.

You can expect a multi-stage interview process. These roles are often in small teams; you may end up speaking, formally or informally, with most of your potential coworkers. It’s an evaluation of personality and fit as much as technical skills.

Your technical skills will also be tested, however. In addition to the usual discussion of past projects and education, you will be faced with practical questions about implementing machine learning processes. You’ll also usually face some tough analytics questions that you may have to solve in code.

Finally, you’ll get big-picture questions about your career intentions and goals. If you are more on the research path, expect questions about your interests and vision.

AI engineer salaries are hefty now. But you’ll want to walk into your interview as a well-informed negotiator. You can get familiar with the current salary standards, typical bonuses, and many of the superlative benefits available to AI engineers today with our AI salary guide.

8. Stay Current on the Latest Developments in Artificial Intelligence and Machine Learning

By the time you get to this stage, you already know that there’s no such thing as resting on your laurels in AI. Breakthroughs in the field happen almost every week. What you were proud to be working on last year is hopelessly outclassed by what the industry is doing this year.

So it’s important to stay on top of your game even after you become an artificial intelligence engineer.

Reading and keeping up with ongoing research are essential. Keeping tabs on the latest pubs to hit arXiv should be second nature. You’ll want to continue to attend conferences and to keep up with your network.

At some point, it may be time to revisit the educational certificate or professional certification question. While you will graduate with a state-of-the-art education and training in AI and ML, at some point in your career you may be faced with the same challenge that a Master’s in CompSci graduate has today: dramatic changes in technology may require major retraining.

Finally, don’t think of your next AI engineer job as your last. It’s common to shift around from company to company. As fortunes change and the leaders in the field shift, you might want to think about moving as well.

No matter where you land or which amazing new projects you decide to pursue, this is a career path that has legs for the 21^st century. You can be assured of plenty of exciting, innovative work. And you have the chance to make a difference in a technology that is sure to alter the course of human history.