Key Takeaways
- ✓AI career growth is concentrated in technical roles that combine math, programming, data fluency, and domain judgment.
- ✓Machine learning engineers typically focus on building, deploying, and maintaining ML systems in products.
- ✓Data scientists often blend modeling with business questions, analytics, experimentation, and stakeholder communication.
- ✓Research scientist and robotics engineer paths can require more specialized graduate training or domain expertise.
- ✓Education options range from bachelor’s and master’s degrees to bootcamps and targeted certificates, depending on your starting point.
Artificial intelligence is playing an increasing role in our daily lives, and there’s never been more people wanting to get involved. Building on data from the US Bureau of Labor Statistics, Georgetown University’s Center for Security and Emerging Technology forecasts the US’s AI workforce to grow by 8% from 2019 to 2029, adding one million jobs to an existing AI workforce of nearly 15 million and doubling the growth rate expected for the country as a whole over the same time frame.
While the next decade will see marketers, product managers, and UX experts flocking to the AI sector to work on exciting new initiatives, the growth CSET forecasts is concentrated in roles that must be filled by someone with technical expertise in AI. The professionals filling these technical positions can be well compensated: Glassdoor has reported median pay for artificial intelligence engineers and machine learning engineers that exceeds the 2020 median US household income.
Even though the field is competitive, an AI career is an exciting prospect if you’re tech-minded: it’s the vanguard of the tech world, and it’s only going to get more interesting. But if you’re just getting started, it can be difficult to figure out where you fit in. What’s the difference between a machine learning engineer and a data scientist? Between an AI research scientist and a robotics engineer? And once you figure out what you’re going for, how can you best position yourself to stand out from the crowd?
That’s where we come in. In this article, we’ll give you the lowdown on popular AI careers: the skills you need, what you can expect to do, and how much you can expect to earn. At the end, we’ll also give you some ideas for how you can start working toward your goal.
“More and more people are going to try to break into artificial intelligence at the same time as companies continue to increase demand for AI research scientists, robotics engineers, data scientists, and machine learning engineers.”— AIFwD Editorial Staff
What skills do you need to work in AI?
We’ll go over the particulars of the different career paths in a moment, but first we want to cover the skills that are common, albeit in varying degrees, across technical roles in the field. In general, an AI professional will need:
Foundation in mathematics
Mathematics, especially statistics and linear algebra, is the key to artificial intelligence: writing, improving, and troubleshooting AI algorithms or machine learning models requires a solid understanding of probability calculations, optimization, vectors, and many other advanced mathematical concepts. This is particularly the case for artificial intelligence subdisciplines like deep learning, where a series of algorithms called a neural network learns how to recognize images, operate prostheses, or even play complex games like Go.
Programming fluency
Most employers require applicants to be fluent in at least one programming language, and in reality, most successful applicants will know several. Popular programming languages in artificial intelligence include Python, R, Java, and C++. AI professionals must also be familiar with coding platforms and environments like PyTorch and TensorFlow.
Data management
In the end, artificial intelligence and machine learning are powered by data, and often by so-called big data: the massive and complex data sets accompanying the proliferation of mobile devices, internet activity, sensors, and cameras across the globe. The importance of data for AI means that AI professionals need to be well acquainted with cloud computing systems large and powerful enough to store and analyze this data, with Amazon Web Services (AWS) being one common example.
Oftentimes, AI professionals must also be experienced with the various techniques and processes along the data pipeline, including data cleansing, which prepares data for analysis; data mining, which probes large data sets for patterns; and data visualization, which helps communicate findings.
Industry expertise
While not required for all jobs, especially when they are entry level, many companies will want incoming employees to have some industry background. An understanding of the industry allows AI professionals to better contextualize their own contributions, implement solutions that have worked elsewhere in the industry, and communicate better with various stakeholders.
Soft skills
While much of the focus when applying for AI jobs is on technical skills, companies are increasingly seeking individuals with strengths in soft skills like critical thinking, leadership, and communication. This should come as no surprise: the World Economic Forum’s list of top skills is heavy with soft skills. As many more people learn to code in the coming years, the differences among applicants will be in how well they can perform in the nontechnical aspects of the job.
What’s the best job for you?
Despite the common skill set, there are important differences in assorted AI careers that are important to take into account. Your goal is to leverage your experience and existing strengths while playing to your interests as you figure out the best path. Below, we’ll lay out the basics of four artificial intelligence jobs with real-world-style examples so you can figure out what might be the best fit. Of course, deciding on a path now doesn’t mean you can’t change your mind later. As you gain experience and learn more about AI, it might surprise you where you end up.
Machine Learning Engineer
A machine learning engineer is responsible for designing, developing, and shipping machine learning models — and then maintaining them once they are shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects.
Machine learning engineers can also be responsible for managing data systems and undertaking various kinds of data analysis, such as analyzing data sets from A/B tests on a machine learning system or algorithm. All this doesn’t take place in a vacuum. ML engineers are tasked with using machine learning skills to solve real-world problems, and their efforts can have a measurable effect on the lives of real people.
For an example of the skills needed, imagine a machine learning engineer working in Chicago to power the search experience at a job-search and review site. An ideal candidate for this kind of position might have a bachelor’s degree or higher in computer science or a related field; two or more years of experience applying machine learning best practices and deploying machine learning models; hands-on experience using structured and unstructured data; natural language processing experience; real-world experience with supervised and unsupervised learning; experience working with big data sets; and strong written and verbal communication skills.
In this kind of role, a successful candidate might collaborate with an engineering team to drive product solutions, use cloud-based tools to develop scalable systems, enhance data pipelines, improve model training and testing, develop algorithms to determine the quality of user-submitted content, and work across teams while overseeing projects end to end.
Is a machine learning engineer position right for you? All AI roles are specialized, but the machine learning engineer role is especially so. While you might work on different products, there will generally be less variety in the kind of work you do. If you are passionate about coding solutions to problems and don’t mind spending hours working through bugs in your algorithms and models, then you might look into machine learning engineer career paths.
Data Scientist
Data science isn’t normally considered a subdiscipline of artificial intelligence, but rather a discipline unto itself, albeit with some overlap. Regardless, data scientist is frequently included among top artificial intelligence careers. In terms of responsibilities, a data scientist ideates and executes novel approaches that turn raw data into business insights and solutions.
After understanding business needs, data scientists determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data. They may then help develop machine learning models and other predictive analytics to efficiently carry out this analysis. After the analysis, a data scientist is usually responsible for communicating results to relevant stakeholders.
To understand the role, imagine a principal data scientist at a large consumer packaged goods, retail, healthcare, or pet-care company. A strong candidate might bring previous experience using data science or data analytics to drive business change; diverse technical experience with statistical analysis; fluency with Python and PySpark; industry experience; and leadership, mentorship, communication, and critical thinking skills.
A qualified candidate in this kind of role might take the lead on data science projects, oversee junior data scientists, implement machine learning techniques and models to analyze data sets, employ data visualizations to communicate findings, and keep business goals at the center of each data science project.
Is a data science career right for you? Data scientists often have a broader set of responsibilities than machine learning engineers. While both can be expected to develop machine learning algorithms and models, a data scientist is often also tasked with designing the research projects in which these models are deployed, and then persuasively communicating the findings to various stakeholders. If you’re a great storyteller equally interested in programming and business intelligence, data science might be the career path for you. For a broader comparison, see our guide to data science vs. machine learning.
Research Scientist
While there is considerable overlap between data scientists and research scientists — both require backgrounds in mathematics, programming, and data management — they differ in the nature of their research. While data scientists are often squarely focused on business intelligence that can be immediately applied, research scientists often work on problems that are more theoretical or have less immediately applicable solutions.
An assistant research scientist working in computer disease simulation modeling at a medical school offers a useful example of how the role’s qualifications and responsibilities might differ from those of a data scientist. A qualified candidate might have at least a master’s degree in a relevant discipline, ideally computer science, applied mathematics, or data science; fluency with Python, R, C, or C++; experience programming Markov models; experience undertaking medical literature reviews; a background in advanced statistics and epidemiology; and proficient communication skills.
A successful candidate might be expected to generate research ideas that build on existing research, manage research projects, develop research techniques and protocols, assist in gathering data and interpreting results of AI computer disease simulation modeling, prepare data sets and findings for publication, and maintain current subject-matter knowledge in fields such as epidemiology and biostatistics.
There is wide variation in research scientist salaries depending on the particular industry or discipline. Salary.com maintains current salary estimates for research scientist roles, but the bigger point is that research scientist compensation depends heavily on sector, credentials, location, and level of specialization.
Is a research scientist career right for you? While data scientists ultimately focus on driving business insights and are often able to move between industries, research scientists focus more intently on contributing to scholarship. Accordingly, they are required to have more specialized subject-matter expertise and employ this expertise not necessarily to influence business decisions, but to push their discipline further. If you already have a passion in the sciences and wish your contributions to be measured not only in dollars and cents but in citations and footnotes, a research scientist position might be a good fit.
Robotics Engineer
As with research scientists, robotics engineers take a core artificial intelligence skill set and combine it with subject-matter expertise in areas like electrical, mechanical, and software engineering. Sometimes, a robotics engineer will also need experience in artificial intelligence subdisciplines like computer vision or natural language processing. This diverse experience allows a robotics engineer to design, develop, and test AI solutions that allow machines to interact autonomously with the world around them.
As an example, imagine a senior robotics engineer in Philadelphia working on software that allows sensors and actuators aboard a ship to communicate with autonomous navigation systems. A senior robotics engineer in this kind of environment might need a master’s degree or PhD in engineering or computer science; significant experience using a programming language like Python to write object-oriented code; active US government security clearance or the ability to receive clearance; experience with autonomous vehicle systems; experience with optimization algorithms, reinforcement learning, and other machine learning and AI concepts; and fluency in AI and data analysis applications and platforms like TensorFlow and Pandas.
A senior robotics engineer with these qualifications might write and test software for advanced autonomous navigation, lead a team to troubleshoot data pipeline and software problems, and consult on optimization of the software architecture for these systems.
Is a robotics engineer career right for you? The robotics engineer path can be daunting because so much is required in addition to core AI skills. Not only do you need deep subject-matter knowledge, you need an engineering background as well. But if you already have a passion for engineering, are interested in working to integrate AI technologies more deeply into our physical world, and are up for a challenge, you might consider becoming a robotics engineer.
How can you get started building an artificial intelligence career?
It’s easy to read through some career paths and pick one that looks appealing, but how do you get started making the dream a reality? The answer in almost all cases is education. If you start getting smart on AI now, tomorrow you’ll already have more opportunities available to you.
For data science, many learners have success with data science bootcamps. If you’re interested in adjacent accelerated training, you can also compare machine learning bootcamps.
If you’re interested in career paths that are heavier on machine learning and artificial intelligence, you may want to consider a bachelor’s degree in computer science, machine learning, or artificial intelligence. You can also start with our guide to artificial intelligence degree requirements to understand common prerequisites and application expectations.
If you already have a bachelor’s degree and are looking to pivot toward an artificial intelligence career, have no fear. Many master’s programs in computer science, artificial intelligence, and machine learning will accept candidates with relevant backgrounds, even if these backgrounds are in other STEM fields. If this sounds like you, compare online master’s in artificial intelligence and online master’s in machine learning options as part of your research.
Frequently Asked Questions
What is the best artificial intelligence career?+
The best AI career depends on your strengths and interests. Machine learning engineering is a strong fit for people who like model building and production systems; data science fits people who enjoy analytics and business questions; research science fits people drawn to scholarship; and robotics engineering fits people who want to combine AI with physical systems.
Do I need a master’s degree for an AI career?+
Not always. Some applied roles accept a bachelor’s degree, portfolio projects, bootcamps, or certificates, especially when candidates can show practical skills. Research-heavy and highly specialized roles may prefer or require a master’s degree or PhD.
Which skills matter most for AI jobs?+
Common AI career skills include statistics, linear algebra, programming, data management, cloud tooling, machine learning frameworks, communication, problem solving, and enough industry knowledge to apply models to real problems.
How should I start preparing for an AI career?+
Start by choosing a target role, then map the required math, programming, data, and domain skills. From there, compare degree programs, bootcamps, certificates, and project-based learning options that match your background and timeline.
Conclusion & Next Steps
Artificial intelligence careers are varied, but the strongest paths usually combine technical depth with a clear understanding of how AI is used in real organizations. Machine learning engineers, data scientists, research scientists, and robotics engineers all work with overlapping foundations, yet each role rewards a different mix of coding, analysis, research, communication, and domain expertise.
If you are still exploring, start with the role that sounds most aligned with your current strengths, then use education, projects, and work experience to test and refine that direction. Your first AI career goal does not have to be your final one.
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