Starting a Career in Artificial Intelligence

Artificial intelligence may revolutionize how people live and work. Learn more about AI career paths aligning with your interests and skills.
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Updated on May 24, 2024
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Although first theorized in the 1950s, artificial intelligence, also known as AI, seemed out of computer scientists' reach for decades. Recent advances show this is no longer the case. Technology such as large language models (LLMs) and text-to-image diffusion software have increased public interest in the field. Private companies and governments plan to reap the technology's potential benefits by investing heavily in AI jobs.

With AI already impacting how people live and work, you may wonder if you need to adjust your career path. Although no one knows the future, you can start researching artificial intelligence careers. Preparing for an AI job may insulate you if AI technology disrupts the job market.

Types of Artificial Intelligence

The term artificial intelligence refers not to one but three types of computer software systems. Computer scientists designed each with a specific task in mind, and their roles may change as AI technology evolves. Understanding the different areas can help you plan your AI career path.

  • Artificial Narrow Intelligence (ANI): You use ANI whenever you ask your phone a question or use your car's self-driving feature. Although very different, these two functions focus on a specific task. ANI is narrow because the software exists to do a single thing.
  • Artificial General Intelligence (AGI): Many computer scientists hope their work in AI leads to AGI, a computer with the same intelligence and self-awareness as a person. This AI advancement could reveal much about how human consciousness and intelligence work.
  • Artificial Super Intelligence (ASI): An AI with ASI would feature a consciousness and intelligence greater than any human. Like AGI, ASI exists only in science fiction as of May 2024. Advances in LLMs, multisensory AI, and neural networks must occur before AGI or ASI can exist.

How Do AI, Machine Learning, Deep Learning, and Data Science All Fit Together?

AI career paths exist in AI and its subfields. You may find well-paying AI jobs in machine learning, deep learning, or data science. Professionals working in these areas collaborate with one another to develop new AI applications.

  • Artificial intelligence uses computer software and hardware to simulate human intelligence. Breakthroughs in machine learning and deep learning affect AI's capabilities and how people interact with it.
  • Machine learning is a subset of artificial intelligence and basically uses data to learn. Engineers create algorithms and statistical models that allow AI to learn without programming.
  • Deep learning is a subset of machine learning that also uses data to learn and adapt. It is analogous to artificial neural networks that model the human brain, meaning the models have many layers of algorithmic structures to imitate how a human would solve problems.
  • Data science overlaps all three areas to draw out relevant data related to its industry. Healthcare, finance, and retail fields all use data science to predict people's behavior. Powerful data science algorithms let LLMs converse with people using natural speech. Large companies and organizations hire data scientists to produce reports, which executives use to make data-driven decisions affecting their organization.
Featured Online Bachelor's in Artificial Intelligence Programs

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Getting an AI Education: Intelligence Required

AI has a high learning curve, but for motivated students, the rewards of an AI career far outweigh the investment of time and energy. Succeeding in the field usually requires a bachelor's degree in computer science or a related discipline such as mathematics.

More senior positions may require a master's or Ph.D. in AI, though some top employers no longer require a college degree. Regardless, your success will depend largely on factors outside a formal education.

Curiosity, confidence, and perseverance are good traits for any student looking to break into an emerging field, and AI is no exception, said Dan Ayoub, general manager for mixed reality education at Microsoft.

A seasoned AI professional, Ayoub agreed to talk to Best Colleges about how to land a job in the field. Unlike careers where a path has been laid over decades, AI is still in its infancy, which means you may have to form your own path and get creative.

What Ayoub means is that there is no standard artificial intelligence degree or curriculum. Some universities may not offer a prescribed set of courses for a major or specialization in AI, while those with dedicated AI programs may have unique approaches to the discipline.

Because AI is still an emerging field ... universities differ in how specialized a degree you may be able to get, Ayoub said. A good place to start is computer science coursework, getting familiar with the basics of data science, machine learning, and Java ... There are a number of new undergraduate and graduate programs popping up every day that are designed to prepare someone specifically to work in AI.

How to Get Into AI: Gain the Skills Needed for an AI Career

Qualifying for an AI job requires mastering basic and specialized artificial intelligence skills. The specific skills you need for career success depend on economic conditions and your career goals.

Basic Skills

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Artificial intelligence opportunities require foundational skills in statistics and general mathematics. Classes in these subjects prepare you for advanced coursework and teach you how the computer science field developed.


Statistics involves collecting, analyzing, and interpreting data. Since the 1970s, statisticians have played a significant role in developing AI software. Without their contributions, AI, such as ChatGPT and other machine learning programs, could not make sense of the vast datasets needed to answer users' questions.

Typical undergraduate statistics classes include probability theory and statistics concepts and reasoning. Your college may let you double major in statistics and computer science.


Top AI degrees emphasize mathematics — current AI lacks advanced reasoning skills. Computer scientists believe mathematics-based breakthroughs in AI could lead to machines with human-like intelligence.

A bachelor's in AI program may require coursework in analytic geometry and calculus, multivariable calculus, linear algebra, and other upper-division mathematics topics.

Specialized Skill Sets and Areas

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Your undergraduate or graduate education can help you build specialized skills on tops of your foundational ones. The specialized skills you learn in school depend on your major and career goals.


Many artificial intelligence careers require fluency in one or more programming languages, such as C++, Java, R, or Python. Programming languages differ in their requirements, development time, and portability to different hardware. You can take an introductory class in different languages before selecting those aligning with your AI career path.

Data Structures and Manipulation

Data structures and manipulation concern the relationship between cases (e.g., a group of people) and variables (e.g., their opinions on a topic). AI uses algorithms to organize and analyze this data. The results can help AI engineers gain new insights into different subjects.

Data structures and manipulation classes emphasize abstract data types and priority queues. Prerequisites may include intermediate or advanced knowledge of C++ or Java.

Machine Learning

Modern machine learning lets computers use data to model the way humans learn. Feeding a computer data allows it to improve its performance or its ability to make predictions. A strong understanding of algorithms is a must in this field.

Deep Learning

With deep learning, AI can analyze data in its raw form (aka unstructured data sets), which includes images, documents, web pages, and audio. Like the human brain, deep-learning neural networks can recognize animals, faces, and other objects by recalling past data and making predictions. Examples of technology using deep learning are voice assistants and self-driving cars.

Data Science

Data science involves solving problems by collecting and analyzing data. You need to be proficient in using a mix of tools and algorithms as well as having a strong grasp of machine learning principles.

Python AI Tool Skills

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Many artificial intelligence opportunities require some knowledge of Python. Learning Python has become very accessible — in some cases, for free. The following code modules, also known as libraries, make using Python for AI and machine learning applications accessible to even inexperienced programmers.


Developed in 2009, pandas helps programmers with data analysis. This open-source software offers many advantages for AI development, including efficient data joining, indexing, and reshaping.

Current pandas AI applications include LLMs. Pandas interprets users' questions and extracts relevant information quickly and accurately. This efficiency lets LLMs mimic natural conversations.


Computer scientists created NumPy in 2005 to help STEM professionals interpret large numerical datasets. NumPy provides users with information quickly due to optimized code. Programmers need a background in linear algebra to make the most of NumPy's functionality.

NumPy helps AI and machine learning programs efficiently make complex calculations. Current applications include climate models and physics simulations.


Scikit-Learn expands AI machine learning's functionality by building upon NumPy fundamentals. This library's algorithms help users classify, regress, and cluster data. Open-source software and free tutorials make using Scikit-Learn accessible to all learners.

Scikit-Learn offers many advantages in AI, such as training models on new data. These exercises help developers spot issues before software releases.


Py-Caret's accessibility makes it an excellent choice for individuals wanting to improve their artificial intelligence skills. The library automates complicated machine-learning workflows with minimal code. This advantage lets programmers spend more time analyzing results.

Py-Caret features many benefits for AI professionals, such as the ability to train, improve, and launch an AI model from a single computer.


Py-Torch combines Python with the Torch library. This intuitive library helps users create neural networks and develop deep-learning models with minimal prototyping. Key features include tensor computation, dynamic graph computation, and automatic differentiation.

The deep-learning models programmers create with Py-Torch help AI applications recognize images and interpret users' speech patterns.


Keras brings simplicity to machine learning programming tasks. This advantage makes developing AI models and interacting with them easier. Flexibility and accessibility make Karas-based AI a valuable tool for researchers at CERN, NASA, and other organizations.

Free guides cover Keras fundamentals and advanced features. New users can download code examples to try out Keras' extensive functionality.


Once you gain experience with the above skills, you can experiment with API, also known as application programming interface. APIs let different software programs interact. (e.g., When you swipe a credit card at a store checkout, an API connects the credit card machine with your bank.)

Commercial API models, such as OpenAI API and Cohere API, let users combine preexisting software with AI functionality. Many industries need API experts who can add AI to their business models.

How Can I Get a Job in AI Without a Degree?

Although many AI career paths require a bachelor's or master's degree, other pathways exist. You can complete an online coding bootcamp, take individual classes leading to a certification, or learn as an apprentice. Studying independently and developing a portfolio can also help you build an impressive resume.

Coding Bootcamp

A coding bootcamp provides essential training in a short time. Introductory bootcamps emphasize computer science fundamentals while not covering the general education curriculum a traditional bachelor's degree requires. Advanced bootcamps focus on specialized topics, including AI.

Some of the best artificial intelligence bootcamps feature flexibility, such as an asynchronous coursework and a fully online curriculum. Both schools and private organizations offer bootcamps, and most last 3-6 months. Choose the right bootcamp by comparing their tuition prices, curriculum, and learning outcomes.

Online Courses and Certifications

An online class can introduce you to artificial intelligence skills at little to no cost. Many free massive open online courses (MOOCs) covering AI topics exist. Although some charge a modest fee, you can read prior students' reviews to find the MOOC that meets your unique needs.

Look beyond MOOCs when exploring online classes. Some leading universities offer classes in AI topics, such as business applications of AI. They may even offer AI certifications.

Internships and Apprenticeships

An internship or apprenticeship can help you gain in-demand skills from highly experienced AI professionals. Many college students complete an internship over summer break. This experience may require temporary relocation and unexpected expenses.

Apprenticeships offer the same advantages as internships while also paying a salary. Apprenticeships last 2-4 years and train you in computer science fundamentals. Top employers, such as Google, offer apprenticeships in different areas, such as data analytics and software engineering.

Work on AI Projects

Develop your artificial intelligence skills by working on small projects posted on online AI communities like Stack Overflow, GitHub, and Reddit. People in these communities can also help you explore AI jobs that align with your interests and career goals.

Creating a work portfolio can help you network and boost your resume. You can present it on a personal website alongside your resume and other relevant information. Research computer science professionals' websites for inspiration.

7 High-Demand AI Career Paths

Data scientists and machine learning engineers' AI breakthroughs are predicted to impact nearly 40% of jobs worldwide in the coming years, according to an assessment by the International Monetary Fund. Projections like this drive the growth of high-demand AI career paths. Companies and organizations wanting to remain at the forefront of innovation offer qualified candidates impressive salaries.

Earning a college degree and creating a portfolio can increase your chances of landing a job in artificial intelligence.

Data Scientist

Data scientists draw valuable conclusions by analyzing large datasets. Their work involves creating data-collection models. Professionals need a solid foundation of business, mathematics, and programming knowledge to succeed in this role.

Top jobs require a bachelor's in data analytics, computer science, or mathematics. According to the Bureau of Labor Statistics, these professionals earn a median salary of $108,020. The best-paying states include Washington, California, and Virginia.

Machine Learning Engineer

Machine learning engineers, also known as machine learning designers, make AI models more accessible and functional for end users. Typical day-to-day tasks include listening to stakeholders' requirements and running simulations. They also identify problems and prototype solutions.

The average salary of machine learning engineers is $118,470, according to Payscale data from April 2024 . Employers offering the highest pay include Meta, Snap Inc., and Adobe. Professionals at these companies can make more than $190,000 per year.

AI Researcher

AI researchers, also known as AI engineers, choose datasets for AI models, develop new AI projects, and train computers on the latest software. Workers spend much of their day collaborating with other engineers. They must also know how to present findings to audiences without a computer science background.

AI researchers need many skills, such as fluency in at least one programming language and an academic background in mathematics. The profession offers a median salary of $128,560, according to April 2024 Payscale data.

Algorithm Engineer

Algorithm engineering career paths involve developing and refining new algorithms. Typical responsibilities include testing algorithms in different AI models to find the best fit. This career appeals to professionals wanting to spend most of their day troubleshooting problems.

You need a bachelor's in software engineering or computer science to qualify for most algorithm engineer positions. Employers may also require fluency in Java or fluency in C++. Algorithm engineers earn an average salary of $121,500 (Payscale, March 2024).

Deep Learning Engineer

Deep learning engineers train neural networks using PyTorch and other Python libraries. Their work improves AI's ability to recognize speech and communicate with users. Other day-to-day tasks include collaborating with software engineers and data scientists.

Most deep learning engineers hold an undergraduate or graduate degree in computer science. They make an average salary of $160,820 (Indeed, May 2024). States with the best-paying jobs include California, Washington, and New York.

NLP Engineer

Natural language processing (NLP) engineers train AI to perform tasks using users' text or speech. Day-to-day responsibilities include creating NLP processing systems, building datasets, and experimenting with algorithms. Like other AI professionals, NLP engineers must keep up with the latest AI and machine learning advances.

You can explore some entry-level careers with only an associate degree. Skills employers look for include a strong background in statistics and programming languages. Professionals earn an average salary of $107,280 (ZipRecruiter, May 2024).

AI Chatbot Developer

AI chatbot developers, also known as AI trainers, teach AI how to communicate with and help users effectively. They predict what questions or tasks users might request. Other responsibilities include creating new datasets, providing training data, and recognizing when an AI cannot interpret data correctly.

A bachelor's in data analytics or data management can qualify you for many entry-level AI chatbot developer positions. The average salary for this career exceeds $93,000 (ZipRecruiter, May 2024).

Frequently Asked Questions About Careers in Artificial Intelligence

How can a beginner start learning AI?

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Start your journey with free introductory classes, such as those offered by Google. Classes like this teach you generative AI and LLM basics. You also learn how professionals in different fields use AI models.

Other introductory class providers include Alison, Coursera, and Udacity. Stanford University also offers introductory AI classes with a fee, but it awards a certificate upon completion.

Can I learn AI without coding?

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Although many low- and no-code AI models exist, you need a solid foundation in at least one coding language to qualify for an AI career path. Work with your college's academic advisors or perform independent research to find out which one can help you launch your career.

How long does it take to learn AI as a beginner?

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Professionals with a mathematics and computer programming background need about a year to master fundamental AI and machine learning skills. A bachelor's in either of these prerequisites takes most full-time degree-seekers four years. You can save money on a bachelor's by attending an in-state public school.

Where can I learn AI for free?

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Major employers and universities offer free or low-cost AI classes for aspiring programmers and professionals interested in using AI in their businesses. Advantages include networking with like-minded people and developing skills you can improve in a bootcamp or college class.

Is AI certification worth it?

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AI certification can improve your resume if you lack work experience or a college degree. Benefits for mid-career professionals include keeping up with the latest AI advances. Earning certifications may also keep you competitive in a rapidly changing job market.

Compare certification programs' reviews and prices before signing up. You should also consider whether you can balance the workload with your other responsibilities.

Note: The insights on this page — excluding school descriptions — were reviewed by an independent third party compensated for their time by BestColleges. Page last reviewed May 14, 2024. is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.

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