Artificial intelligence is an emergent field. There’s no question that it’s a profession that is still finding its groove. There aren’t clear standards yet to help orient jobseekers in terms of:
- Consistent job title and task alignment
- Settled educational standards
- Common professional certifications
So if you have questions about launching a career in AI, you’re not alone. It’s basically like training to become a pilot a couple years after the Wright Brothers took off. No one knows what it will turn into over the years!
On the other side of that uncertainty is the excitement of getting into a profession where you can play a role in defining the field for future generations.
Taking a job in a brand-new field like artificial intelligence means that you will be among the people defining what AI careers are all about.
That doesn’t mean that absolutely everything is unknown, however. People are landing jobs in AI today. And it’s an industry growing out of broadly similar fields like data science and information technology. There is definitely some well-earned knowledge to pass around already.
For the best answers that we have for these questions today, keep reading!
How to start a career in artificial intelligence?
Get an education. There is no substitute for knowledge in one of the most complex fields that humankind has ever delved into.
Learning what you need to know for success in AI can start as early as high school. The groundwork comes through studies in:
- Mathematics - In particular, the highest level of algebra you can take and as deep into calculus will jump start you in this vital skill in AI engineering.
- Programming - Computer science classes that introduce basic programming principles and structures lay groundwork for learning languages that are important in AI engineering
- Statistics - Not all schools offer a class in statistics, but some offer it as preparation for the Advanced Placement Statistics exam
Depending on what direction you want to take your career, there are also often high school options that offer some practical experience. Many schools, for example, have robotics clubs, or computer clubs. Even chess clubs can teach valuable reasoning and logic skills.
Studies in philosophy and epistemology have surprising applications in artificial intelligence careers.
Other areas to study for an AI career may depend on what specialization you plan to pursue. Chemistry and biology can be valuable preparation for paths into biotech or medical AI jobs. Video or even art classes can help in a transition to computer vision AI.
You’ll also need a formal degree, preferably in the field of artificial intelligence or machine learning. Not only do these continue your education, but they also begin to create a professional network and open up opportunities that may lead to jobs.
Professional certification is valuable throughout the technology industry. Pursuing and earning AI or ML professional certifications can help shift your resume to the top of the pile when it comes time to apply for jobs in the field.
How do you become an AI engineer?
There are eight steps on the typical path to an AI engineering career:
- Learn about the job – These are new positions with demanding requirements and skills. You should understand what a day in the life of an AI engineer looks like before you pursue it seriously.
- Pick one of the three career tracks in AI today – Research and science, practical engineering, or specialized applications all lead to different work environments and require different preparation. Find your focus early.
- Earn a relevant bachelor’s degree – Strong foundations in math, statistics, and programming are set at the undergraduate level. Pick the right degree to prepare.
- Earn a master’s degree in AI or ML – A graduate degree develops the advanced skills in building self-learning algorithms, honing large language models, or developing image recognition routines that AI engineers apply to the practical challenges they face.
- Take on AI projects to develop hands-on expertise – Engineering is a field that applies theory to the real-world. The only way to learn it is to do it, so engaging in projects in or out of school to build expertise is crucial.
- Earn a professional certification in AI or ML – Professional certifications signal to employers that you have up-to-date skills in specific technologies or techniques in AI or ML. Earning a cert helps float your resume to the top of the stack.
- Get a job as an AI engineer – With the right qualifications, it’s not tough to find jobs in AI engineering today. You’ll be more interested in finding the right fit for your interests and credentials.
- Stay current in a fast-changing field – AI engineering changes every day. Once you get a job as an AI engineer, you’ll have to stay up-to-date to keep it.
Fortunately for you, we’ve written an in-depth How to Become an AI Engineer Guide outlining all these steps in detail.
How to get a job in AI without a degree in the field?
The best way to get a job in artificial intelligence without earning a degree in the field is by earning a certificate in artificial intelligence or machine learning.
Educational certificates are compressed versions of the coursework required for a full AI degree. They are offered at both the baccalaureate and graduate level. They will typically last less than a year and have only a half dozen or fewer classes. Of course, that also means they cost much less than a full degree.
Those classes are designed to build on studies in math, statistics, and computer science. So you should have a degree that is at least in some STEM field in order to be prepared for AI certificate studies. Many certificates, particularly at the graduate level, will have specific prerequisites for entry. Those might include classes in database systems or even introductory ML algorithm design. You need coding proficiency and advanced math skills.
Another path to AI careers for some people already in the tech field is by taking an AI bootcamp course. Designed to pack a lot of focused learning into a very short span, these are intensive and practical training courses with a lot of applied and hands-on learning. They offer a shortcut to AI skills, but don’t come with a lot of background knowledge.
How can you become an AI engineer without a degree?
Be a genius.
On top of your towering intellect, you had also better be extraordinarily motivated and have the work ethic of a Clydesdale.
No, seriously, there are just no AI engineering jobs listed today that do not mention degree requirements. You absolutely need a bachelor’s degree at a minimum. A master’s degree is a more common requirement, and there are even some AI engineering jobs where only a PhD in Artificial Intelligence will do.
On the other hand, the degree need not be in artificial intelligence itself, or even machine learning. Many people are working quite successfully in AI engineering roles today with degrees in fields such as:
- Computer Science
- Data Science
- Engineering
- Computational Linguistics
- Physics
There are also successful AI engineers who never technically graduated from college and therefore don’t hold a degree. But it’s not the piece of paper that’s the important part. They got a formal education in important subjects along the way to fuel that success.
This is not to say that it is impossible to find any jobs working in the field of artificial intelligence without a college degree. But those jobs will not be AI engineering positions. If you are interested in prompt engineering, AI training, or even software development jobs that implement the front-end interface to AI tools that other people have built, you’re in luck. The window is narrow, but it’s possible through self-study, experience, and a lot of lucky breaks to land these sorts of positions without degrees.
What are the most important skills in the field of AI?
Artificial intelligence has many applications in many industries. The right mix of skills for any position in any of those various industries may be unique. But the core knowledge and abilities that fuel all AI today are the critical foundations they all build on:
- Mathematics is hands-down the most important skill any AI engineer brings to the table. AI techniques require an understanding of linear algebra concepts like tensors, eigenvectors, and principal component analysis. You also need a mastery of calculus derivatives, matrix calculations, and gradient algorithms.
- Statistics and probability represent a superset of those math skills that are also needed. Bayes’ theorem and common distributions like binomial, Gaussian, and exponential are all concepts AI engineers rely on. Information theory also falls out of this, with an understanding of entropy, Kullback-Leibler divergence, and how to calculate them all being critical.
- Computer programming skills come in a distant third. AI engineers have to be able to read and write in at least one programming language. They need the ability to:
- Build algorithms
- Access and manipulate data structures
- Architect basic routines implementing AI and ML
- Finally, domain expertise in a specific field is usually important. Although some work in artificial intelligence is pure computer science and research focused, many jobs are oriented at AI applications in specific fields. Understanding the important aspects of those fields and how AI can be useful is the difference between success and failure.
Of course, these are exactly the kind of subjects that are covered in AI degree programs at every level.
What are the main career paths in AI?
Artificial intelligence career paths are still unwinding as the industry finds its footing. Today, it seems to be sorting itself out into three general lanes:
- Computer science and research focused - These are the pure research and development careers that are breaking new ground in AI capabilities and concepts. They are mostly found in academia, major information technology companies, or hot new startups aiming to change the game. The kinds of degrees that are most aligned with this field are on the high end:
- Master of Science in Machine Learning
- PhD in Computer Science AI Concentration
- Job titles in computer science and research:
- AI Research Scientist
- Research Engineer for Superalignment
- Deep Learning Performance Architect
- Business and government AI applications focused - Putting those new developments to use in real-world businesses and government agencies is the second career path. These deal with more practical uses of artificial intelligence. Degrees may include:
- Bachelor of Science in Artificial Intelligence
- Master of Engineering in Artificial Intelligence
- Master of Science in Business Intelligence and Data Analytics
- Job titles in business and government AI:
- AI Solutions Engineer
- Prompt Engineer
- Generative AI Engineer
- Specialized AI uses in specific professional fields - Finally, there is a third track that exists in highly technical professional applications of AI for critical, specific tasks in certain fields like healthcare and transportation. These require high domain expertise in those areas and a strong skillset in ML and AI. Degrees for this track are highly specific to the profession in question, such as:
- Master of Science in Artificial Intelligence Bioinformatics concentration
- Master of Engineering in Robotics with AI and Machine Learning Specialization
- Master of Science in Cybersecurity Artificial Intelligence Specialization
- Job titles in specialized professional uses of AI:
- Computational Biologist
- Autonomous Vehicle Engineer
- Radiology Data Annotator
- Intelligent Transportation System Manager
There will be plenty of opportunities to cross over between these general tracks for anyone with the right credentials in AI engineering. There are also plenty of areas of specialization even within those larger tracks. An entire career can be built around autonomous vehicles or computational biology, for example.
What careers are there in AI?
The field of artificial intelligence comes with both variations on common and well-known technology and business careers as well as some genuinely new and groundbreaking kinds of jobs.
Traditional careers that are emerging with new responsibilities in the world of AI include:
- Software developers and architects - Programmers and system designers work to build AI software and connect it to existing apps and services.
- Product and project managers - Just like other kinds of software, jobs defining a product and organizing the many people working on it are necessary in AI.
- Consultants - With big demand for AI expertise and not enough qualified engineers to go around, consultants are making careers out of advising businesses on new developments in the field.
- Systems analysts - Analysts examine the impacts of AI on various social and business areas, as well as evaluate AI systems themselves.
The field is also creating some entirely new kinds of jobs, such as:
- Prompt engineer - While generative AI has gotten pretty good at understanding plain English, there are still special techniques being used to get the most accurate and effective answers from those systems. Prompt engineers are experts at crafting the right query to maximize the response from AI systems.
- AI trainer - Like a puppy or a child, machine learning algorithms don’t come with a built-in knowledge of the world—they must be trained to perform their tasks. AI trainers are experts in offering the right kind of data and shaping feedback to the system to build it into an effective product.
- Computer vision engineer - People take for granted how much of our understanding of the world emerges from visual perceptions. For computers to develop similar knowledge and use it to reason through real-world problems and tasks, they must be trained to perceive and understand images. CV engineers combine an understanding of AI processing, the physics of light, and the mechanics of camera systems to bring that ability to machines from robots to satellites.
Examples of these positions, or variations on these roles, are available in each of the three main career paths in AI today:
- Computer science and research
- Business and government applications
- Specialized AI uses in key professional fields
For a more complete list, plus educational requirements, salary ranges, and more, check out our AI careers page.
What is an AI engineer?
Artificial intelligence engineers are experts in machine learning, computer science, programming, and data science who take advanced AI theories and techniques and turn them into practical solutions to real-world challenges.
Engineering is all about the application of pure science and theory to the messier parts of reality. Engineers in artificial intelligence use the tools and techniques of thinking machines to overcome obstacles ranging from interpretation of vast amounts of data to building robots for complex and dangerous industrial tasks to allowing computers to understand and reply to plain English sentences.
The education needed to bring the power of science out into working tools in society requires learning both the theory and the reality. Although engineering is a practical discipline, it requires creativity and innovation. AI engineers take on different challenges in every industry they work in, but as AI advances, they will find roles in virtually every industry that exists.
What is the highest paying career in AI?
AI engineers who work in robotics and hardware/software interfaces are the most highly paid artificial intelligence jobs today.
The answer to this question is far from definitive right now, however. Few good sources of data exist on salary and employment for a career field as new as AI. In many cases, the jobs that have popped up in the field aren’t even recorded by official sources yet. Private salary surveys exist, but do not provide a complete picture.
However, it seems clear that most careers in AI today pay in the six-figure range. The specifics vary by role and industry of employment, as well as location. You can find a lot more information and hard numbers in our AI Salary Guide.
What jobs can you get with an artificial intelligence degree?
There are a wide range of different careers available to anyone who graduates with a degree in AI today.
In part, the jobs that are available will depend on the level of degree you earn. It takes a graduate degree to qualify for the more high-paying technical positions. Some entry-level or implementation positions can be had with a bachelor’s degree, however.
Just a taste of the different jobs available include:
- AI Engineer
- AI Software Developer
- AI Trainer
- Prompt Engineer
- AI Research Scientist
- Computer Vision Engineer
For a more complete picture of the jobs available with an AI degree, as well as the industries where they can be found and the salary range they offer, check out our AI Career Guide.
Will AI take over software engineering?
Not exactly! Take over is a little too strong. But AI certainly will profoundly change how software engineering happens.
Natural language processing is one of the major areas in which AI has developed incredible new capabilities. Computer code is also a language, although not a very natural one for most people. But just as NLP has created the ability to quickly and accurately translate from, say, Japanese to English, it also offers the ability to translate from English to Java, Python, or C++… any kind of computer language it is trained on.
This has already led to programming tools such as Copilot that can suggest improvements or even write entire functions based on plain English prompts. While those tools assist developers in IDEs (Integrated Development Environments), some experimenters have also had success simply asking regular chat bots to write working code.
Some researchers suggest that within twenty years, computers will be writing most of their own code.
As the name of the profession suggests, though, there is more going on in software engineering than just writing code. In fact, much of the actual work of devs goes into performing their own sorts of translation. The ideas for programs that come to them aren’t often logically consistent, clearly stated, or even practical. Much of their work is in producing specifications, investigating use cases, exploring synergies and solutions that make creative use of current code or tools.
To duplicate all those efforts, AI will have to advance well beyond just spitting out code. It will need far more general reasoning skills, and the ability to intuit meaning and unstated assumptions.
While AI may end up taking over the last mile of software development by actually laying down the code that will be executed, software engineering as a whole will still have human workers for a long while.