Artificial Intelligence Careers: What’s the Best Job for You?

Even though the field is competitive, an AI career is an exciting prospect if you’re tech-minded: it’s truly 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 the most 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 towards your goal. That’s a lot to cover, so let’s dig in.

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 the technical roles in the field. In general, an AI professional will need:

Foundation in mathematics

Mathematics, especially statistics and linear algebra, are the key to artificial intelligence: writing, improving, and trouble-shooting 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, the realm of machine 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 so-called big data, the massive and complex data sets accompanying the proliferation of mobile devices, internet activity, and various 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 — Amazon Web Services (AWS) being the most popular. Oftentimes, they 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 to communicate findings.

Industry expertise

While not required for all jobs, especially when they are entry-level, many companies will want their 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 10 Skills of 2025 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 non-technical aspects of the job.

What’s the best job for you?

Despite the common skillset, there are important differences in the assorted AI careers that are important to take into account to ensure that you are leveraging your experience and existing strength and playing to your interests as you figure out the best path for you. Below, we’ll lay out the basics of five artificial intelligence jobs with real-world 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

Overview

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. In addition, machine learning engineers can 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 so their efforts can have a measurable effect on the lives of real people.

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 jobs.

Data Scientist

Overview

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, they determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data, and 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.

Is a data science career right for you?

As you can see from the Mars position, 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. Oftentimes, a data scientist will have to translate complicated, interdisciplinary research in a way that is equally understandable regardless of whether a coworker is a product manager, a marketer, or even a CFO. So while machine learning engineers will often spend more time coding, data scientists will often spend a greater amount of time thinking about business goals. If you’re a great storyteller equally interested in programming as you are in business intelligence, data science might be the career path for you.

Research Scientist

Overview

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, as we’ve seen, are often squarely focused on business intelligence that can be immediately applied, research scientists often work on problems that are more theoretical, with less immediately applicable solutions.

Is a research scientist career right for you?

From the requirements and responsibilities for the assistant research scientist position at NYU Langone, it’s clear that the role differs from the data scientist role both in ultimate outcome and necessary areas of expertise. While data scientists ultimately focus on driving business insights, and so are more easily 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, are also interested in statistics, programming, and data management, and wish your contributions to be measured not in dollars and cents but in citations and footnotes, a research scientist position might be a good fit.

Robotics Engineer

Overview

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 then test AI solutions that allow machines to interact autonomously with the world around them.

Senior Robotics Engineer, TDI Novus, Inc.

As an example, let’s take a senior robotics engineer working for TDI Novus in Philadelphia on software that allows sensors and actuators aboard a ship to communicate with autonomous navigation systems. TDI Novus wants their senior robotics engineer to have, among others, the following qualifications:

• A master’s degree or PhD in some field of 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

• Fluency in AI and data analysis applications and platforms like TensorFlow and Pandas 

TDI Novus would expect a senior robotics engineer with these qualifications to be responsible for:

• Using Python to write and test software allowing for advanced autonomous ship-navigation

• Leading a team to troubleshoot and develop solutions to various problems in the software and data pipelines of these autonomous navigation systems as they arise

• Consulting on optimization of software architecture for these systems

The AI professional in this position could expect to earn $115,000 to $145,000 a year with full benefits.

Is a robotics engineer career right for you?

The robotics engineer path can be daunting: it’s no surprise that the TDI Novus position requires an advanced degree, because so much is required in addition to the 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 have success with data science bootcamps. If you’re interested in those, you can check out our list of recommendations to see if one strikes your fancy. 

If you’re interested in career paths that are heavier on machine learning and artificial intelligence, you probably want to go for a bachelor’s degree in computer science, machine learning, or artificial intelligence. Again, we have recommendations for programs that might work for you.

If you already have a bachelor’s degree and are looking to pivot towards an artificial intelligence career, have no fear! Lots of 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, hit up our suggestions for the best AI & ML master’s programs.