The 5 Fastest-Growing Jobs in Artificial Intelligence and How to Land Them

Artificial intelligence is a fast-growing field with many exciting opportunities. Here are five AI jobs to consider, along with tips on how to land them.
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The World Economic Forum predicts that by 2025, the time spent on current tasks in the workforce by humans and machines will be equal.

There's no doubt that artificial intelligence, or AI, is a fast-growing industry with an immense impact on our future.

If this field excites you, there's a huge variety of artificial intelligence jobs in different industries you can pursue. AI is becoming prevalent in many industries, including healthcare, manufacturing, transportation, education, and cybersecurity.

If you want an AI job but haven't worked in the field before, a master's degree in applied artificial intelligence is a great way to switch your career, develop expertise, and increase your opportunities.

Discover the five fastest-growing AI jobs and get tips on landing a role from an industry expert.

1. Data Scientist

Median Annual Salary (May 2021)

Minimum Education Requirements
Bachelor's degree

Data science careers are projected to grow a whopping 36% between 2021 and 2031, according to the Bureau of Labor Statistics (BLS).

The AI industry relies on data scientists to use math, statistics, data mining, visualization, and cluster analysis to optimize performance and solve problems. Responsibilities include analyzing customer trends, financial analysis, and process improvement.

Data scientists are commonly called upon to help understand the way that a business is functioning, said Anna Marbut, a faculty member in the University of San Diego's MS in Applied Artificial Intelligence program. This may include answering questions about marketing efforts, software performance, revenue streams, and more.

You'll need, at a minimum, a bachelor's degree to enter the data science field. However, a master's in applied artificial intelligence can equip you with the advanced skills you'll need to thrive in the AI industry.

Common tools for a data scientist are changing from statistics and regressions to calculus and neural nets, explained Marbut. Data scientists who can apply deep learning methods to solving business problems will have a leg up on their more traditionally educated peers.

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2. Machine Learning Engineer

Average Annual Base Salary (2019)

Minimum Education Requirements
Bachelor's degree

Machine learning engineer was ranked No. 1 on Indeed's best jobs of 2019 list, with 344% growth between 2015 and 2018.

A machine learning engineer may develop AI software for search engines, chatbots, translation apps, virtual assistants, and self-driving cars.

According to Marbut, machine learning engineers in the AI industry are tasked with building, training, and serving AI models for larger software products.

As an engineer, you'll also develop machine learning experiments, implement algorithms, and ensure that the user experience is optimal.

More senior roles may require a master's degree. Earning an advanced AI degree can qualify you for leadership positions while deepening your expertise.

Students in a master's in applied AI program will gain practical experience in applying deep learning techniques to real-life problems and will be familiar with how these methods can plug into a larger software product, said Marbut.

3. Data Engineer

Average Annual Base Salary (Oct. 2022)

Minimum Education Requirements
Bachelor's degree

The 2020 Dice Tech Job Report found 50% job growth for data engineering in just one year.

Becoming a data engineer is a popular route for those wishing to work for tech companies and in artificial intelligence.

These professionals transform raw data into actionable information and are responsible for understanding and managing the flow of data across an entire company, according to Marbut.

A data engineer in an artificial intelligence role supports data storage and transfers across the business.

This often involves AI-dependent data transformation pipelines, managing computer resources, and providing data access to all parts of a business, said Marbut.

4. Computer and Information Research Scientist

Median Annual Salary (May 2021)

Minimum Education Requirements
Master's degree

Think of this job as the Thomas Edison of AI. Computer and information research scientists are theorists, inventors, and designers of technology.

The BLS projects a 21% job growth for computer and information research scientists between 2021 and 2031 — far faster than the average projected growth for all jobs.

In this career, you can invent or design new tech and find new ways to use existing tech. In AI specifically, computer and information research scientists apply innovative processes to robots and virtual assistants.

A computer and information research scientist may work on projects that include everything from training a model to write its own computer code to training a model that will control the motors in a physical robot, said Marbut.

Most careers in computer and information research science require a master's degree. Choosing to specialize in applied artificial intelligence will equip you to tackle technology's latest advances.

Computer and information research scientists are tasked with creating new ways for computers to interface with the world, and almost all of the recent innovations in this field are based on AI and deep learning methods, said Marbut.

5. Applied Scientist

Average Annual Base Salary (Oct. 2022)

Minimum Education Requirements
Master's degree

Applied scientists focus on using technology to solve problems that consumers or businesses face in the real world. They work with other departments to find functional applications for their research.

This kind of research may involve optimizing an existing AI application, such as a search engine or an automated factory quality control system, explained Marbut. Or it may involve the creation of a new type of model that can improve on non-AI methods, such as a biometric security system or self-driving cars.

With a master's degree in artificial intelligence, you'll become familiar with the deep learning methods and applications needed to innovate in today's business environment.

How to Get a Job in Artificial Intelligence: 3 Essential Tips

There are many paths to working in artificial intelligence. An advanced degree in AI, relevant work experience, and a strong network can help get you there.

Earn a Master's Degree

A master's degree in applied artificial intelligence is a useful asset for your career in AI. An advanced degree can help you qualify for leadership positions, increase your salary potential, and sharpen your expertise.

If you already hold a more general bachelor's degree, a master's in artificial intelligence can help you learn about the AI industry specifically.

Students will have been exposed to all of the most common deep learning model architectures and, most importantly, will have had experience using these models with real-world datasets and applications, explained Marbut when discussing the value of a master's in applied AI.

Our program focuses on making sure that students leave the program not only knowing what the most common models are but how to implement them in a business context, added Marbut.

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Gain Work Experience

A resume that lists your applicable work experience and projects is essential for landing a competitive job in artificial intelligence.

Students should be active on Github and have a diverse portfolio of projects that they've worked on, advised Marbut.

Fortunately, you don't need experience in the AI field to qualify for an artificial intelligence job. The technical skills and knowledge you collect from other tech jobs can apply to AI roles in useful ways.

If a job applicant has only worked in one industry or even at a single company, they will still be a very strong candidate for a new role if they can demonstrate knowledge and experience with a variety of model architectures and implementations, said Marbut.

If you want to transition to the AI industry from a different tech field, seek out projects and opportunities that allow you to build transferable skills and experience.

Find Mentors and Network

Connecting with other professionals in the artificial intelligence industry can lead to job opportunities and professional growth.

One piece of advice? Seek out mentorship related to AI and maintain professional relationships with your peers.

As in most fields, personal testimonials and connections are often what differentiates one great candidate from the next, said Marbut.

Recommendations can go a long way in helping you get an interview or a job offer.

Keeping up with the industry is also essential to being successful in AI. Marbut noted how the field moves lightning-fast: Papers that were groundbreaking five years ago are now old news, and keeping up with the latest methods could be a full-time job in itself.

You can start building your network in school by connecting with peers and professors.

Throwing yourself headfirst into the artificial intelligence community will also likely lead to connections that can help you find jobs in the field and advance your career.

It's always a good idea to attend AI conferences, like NeurIPS or AAAI, as they will be the platform for presenting much of the groundbreaking work each year, said Marbut.

With Advice From:

Portrait of Anna Marbut

Anna Marbut

Anna Marbut is a full-time professor of practice in the MS in Applied Artificial Intelligence program at the University of San Diego. Marbut is also a Ph.D. candidate in computational linguistics through the Department of Interdisciplinary Studies at the University of Montana. She previously worked as a machine learning engineer/data scientist at Submittable, a Missoula-based SaaS company.

Marbut's research focuses on incorporating human-curated knowledge bases into natural language processing models and exploring the geometric properties of language models' learned latent spaces. Marbut worked at Submittable throughout her master's and the beginning of her Ph.D. program, and has experience with web scraping, creating executive-level analyses and recommendations, data modeling, training and productionizing machine learning models, and everything in between.