Artificial intelligence (AI) has become part of how nearly every industry gets its work done, from diagnosing patients and detecting fraud to editing news and processing benefits claims. Companies across diverse fields are competing for the same limited pool of people who can build, train, and manage AI systems, but the talent isn’t keeping up with the demand.
The catch is that “a career in AI” describes dozens of different jobs. Someone who trains machine learning models, someone who studies the ethics of automated decisions, and someone who programs robots all work in AI, yet their days look nothing alike. Each job also has its own entry point, usually a specific graduate degree, so the decision starts with knowing which kind of AI work fits you.
Key Takeaways
- AI careers include at least six distinct specializations, from machine learning and data science to robotics, computer vision, AI software engineering, and AI ethics, with each suited to a different set of strengths.
- Demand and pay far exceed the national average for all jobs, with data scientist roles projected to grow 34 percent through 2034 and median wages from $102,320 to $140,910 across the occupations AI work draws on, according to the U.S. Bureau of Labor Statistics.
- Your specialization points to a specific type of graduate degree: Applied AI for deployment, the MS in AI for research, computer science for software engineering, and data science for analytics.
- Pace University’s Seidenberg School offers all four in New York City, with online options in computer science and data science.
Areas of AI Specialization
Federal wage and growth data shows why AI skills command attention. Across the occupations that AI work draws on, median wages far exceed the national average of $49,500, and several rank among the fastest-growing jobs the U.S. Bureau of Labor Statistics tracks.
The table below lists the occupations most relevant to AI careers; because many AI roles are too new for their own category, these serve as the closest available benchmarks rather than exact figures, and published estimates for specialized roles often climb higher.
| BLS occupation | Median wage | Projected growth (through 2034) | Relevant specializations |
|---|---|---|---|
| Data scientist | $112,590 | 34% (4th-fastest overall) | Machine learning, NLP, data science, computer vision |
| Computer and information research scientist | $140,910 | 20% | Machine learning, AI research |
| Software developer | $133,080 | 15% | AI software engineering |
| Mechanical engineer (nearest match for robotics) | $102,320 | 9% | Robotics |
Underneath those headline numbers are half a dozen distinct areas of AI work. Each one draws on a different mix of skills, appeals to a different kind of thinker, and lines up with a specific graduate degree.
Machine Learning
Machine learning is the foundation of modern AI. Engineers in this area develop the systems that learn patterns from data and sharpen their predictions over time, from recommendation engines to fraud detection to medical imaging tools. The work blends software engineering with applied statistics, so you write production code while also choosing algorithms, tuning models, and deciding what “accurate enough” means for a given problem.
| Common Roles | Core Skills |
|---|---|
| Machine learning engineer, ML operations (MLOps) engineer, applied scientist | Python, frameworks like TensorFlow or PyTorch, strong applied math |
Natural Language Processing
Natural language processing (NLP) is the branch of AI that teaches machines to work with human language, whether reading it, interpreting it, or generating it. Every time a chatbot answers a question, a search engine interprets a vaguely worded query, or a tool summarizes a long document, NLP is what makes that possible. The rise of large language models (LLMs) has pushed demand for this area sharply upward, because the companies deploying those models need people who can fine-tune them, evaluate their output, and keep them accurate in production.
| Common Roles | Core Skills |
|---|---|
| NLP engineer, computational linguist, conversational AI developer | Machine learning, linguistics, strong Python, transformer models |
Data Science and Analytics
Data science turns raw information into decisions. Professionals in this area collect, clean, and model large datasets to answer questions a business cannot settle by intuition alone: which customers are about to leave, where a supply chain will bottleneck, or what a medical trial’s results actually mean. In an AI context, data scientists also assemble the training datasets that machine learning models depend on, which makes this one of the most direct routes into applied AI.
| Common Roles | Core Skills |
|---|---|
| Data scientist, data analyst, machine learning analyst | Python or R, SQL, statistics, AI libraries such as TensorFlow |
Robotics
Robotics gives AI a physical form. Engineers in this field build machines that sense their surroundings and respond to them, from warehouse systems that pick and sort inventory to surgical assistants to autonomous drones. The work extends past software into control systems, sensor integration, and hardware, so a robotics engineer has to understand both the code that makes a decision and the physical motors that execute it.
Researchers in human-robot interaction focus on how people and machines work together. At Pace University, Sara Falcone, PhD, an assistant professor of computer science whose work combines human-robot interaction and embodied AI, earned an Alfred P. Sloan Foundation grant to make hybrid conferences, lab visits, and talks more accessible for remote participants. “We’re building lightweight wearable haptic prototypes worn on the forearms and shoulders that deliver haptic cues as a low-disruption, private communication channel alongside audio/video,” Falcone said. “The goal is to support things like attention management, timing and turn-taking, navigation and awareness in physical spaces, and to smooth social coordination in busy hybrid settings.”
| Common Roles | Core Skills |
|---|---|
| Robotics engineer, automation engineer, controls engineer | Machine learning, real-time programming, mechanical and control fundamentals |
Computer Vision
Computer vision teaches machines to interpret what they see. A trained model can read handwritten forms, flag a manufacturing defect a human eye would miss, guide a self-driving car around obstacles, or scan medical images for early signs of disease. As cameras and sensors turn up in more products, demand for people who can develop this kind of perception has grown across healthcare, automotive, retail, and security.
| Common Roles | Core Skills |
|---|---|
| Computer vision engineer, perception engineer, imaging scientist | Deep learning, libraries such as OpenCV and PyTorch, image-processing math |
AI Software Engineering
Behind every AI product is the engineering that makes it usable: the APIs, pipelines, and infrastructure that move a model from a researcher’s notebook into an application millions of people can open on their phones. AI software engineers write that connective code. They integrate models into products, scale systems to handle traffic, and keep everything stable once it ships.
| Common Roles | Core Skills |
|---|---|
| AI software engineer, machine learning infrastructure engineer, backend engineer | General programming, cloud and systems knowledge, model behavior in production |
AI Ethics and Governance
As AI systems decide who gets a loan, how a résumé is ranked, or what a medical algorithm flags, organizations need people who can judge whether those systems are fair, transparent, and legally compliant. AI ethics and governance is the youngest of these specialties, and one of the fastest to gain ground as regulation catches up with the technology.
The people in these roles translate legal requirements and ethical principles into standards that engineering teams can follow. It’s also active research territory at Pace, where Professor Christelle Scharff, PhD, and her students expanded an image dataset to include African fashion so AI systems would stop misclassifying non-Western garments.
Soheyla Amirian, PhD, an assistant professor of computer science who leads the Applied Machine Intelligence Initiatives and Education Laboratory, brings the same scrutiny to AI in healthcare. “The objective is to develop algorithms that minimize bias related to sex, ethnicity, or socioeconomic status, ensuring equitable healthcare outcomes,” Amirian said. “This work is guided by the principle that AI should benefit everyone, not just a privileged few.”
Because the specialty is so new, federal wage data has yet to catch up. Technical responsible-AI work is closely related to the research-scientist and data-science figures above, while policy-facing governance roles align more with compliance and management occupations.
| Common Roles | Core Skills |
|---|---|
| AI ethicist, responsible AI lead, AI governance specialist | Understanding of how models are built, plus policy, law, or philosophy |
Matching an AI Specialization to a Pace Graduate Degree
Once you know which kind of AI work suits you, the next step is choosing a program that trains you for it. Pace University’s Seidenberg School of Computer Science and Information Systems has taught AI for more than 30 years, with over 15 AI faculty experts and more than 40 courses that build AI across its programs. Across the school, 93 percent of Seidenberg students are employed, continuing their education, or pursuing service within one year of graduation.
Four graduate degrees anchor Pace’s AI offerings, with one built for applied work, one for research, one for core computing and software, and one for data science.
MS in Applied Artificial Intelligence
The Master of Science in Applied AI is the most hands-on of the four. You learn to design, deploy, and manage AI systems that produce real results, building things like retrieval-augmented chatbots, forecasting pipelines, and decision dashboards along the way. A required ethics course also makes it a solid route into responsible-AI and governance work.
The program offers three concentrations that enable you to tailor the degree around your goals:
- Human-centric AI for user experience and human-computer interaction
- Data-centric AI for large-scale data work
- Computational Intelligence for machine learning, NLP, and computer vision
If you want to put AI into production across a field such as healthcare, finance, or media, this is the program for you.
MS in Artificial Intelligence
Where the Applied AI degree points toward production, the Master of Science in AI points toward research. You go deep into machine learning, natural language processing, robotics, computer vision, and generative AI, then put the theory to work on faculty-led projects at the Pace AI Lab, the Robotics Lab, and the Augmented Intelligence Lab.
Courses including Pattern Recognition and Advanced AI get into the methods behind visual recognition and reinforcement learning. Anyone drawn to the research end of the field will find a fit here, whether the goal is a research scientist role, an NLP or computer vision specialty, or doctoral study later on.
MS in Computer Science
Pace’s computer science master’s degree helps you build the engineering foundation that AI software work depends on. The core includes algorithms, cloud and web computing, and database systems, with AI and data science available through electives.
Flexibility sets this one apart. You can study on campus or fully online, full or part time, and a single Java bridge course opens the door to people without a computing background. For anyone targeting AI software engineering, machine learning infrastructure, or a systems-level role, it offers the deepest grounding in how software gets built.
MS in Data Science
The data science master’s program teaches you how to turn large, unorganized datasets into decisions. You work in Python, R, TensorFlow, Spark, and Hadoop, learn the statistics and modeling that data roles demand, and graduate with a portfolio built on real data.
Courses in artificial intelligence and deep learning connect the analytics work to modern AI methods. Like computer science, the program is offered on campus and online, which suits working professionals. If your target is data science, analytics, or machine learning modeling, this one is the ideal program.
At a Glance
Each degree fits some specializations better than others, though several overlap in practice.
| Degree | Prepares you for | Career paths | Format | Program highlights |
|---|---|---|---|---|
| Applied AI, MS | Building and deploying AI systems | ML engineer, AI product manager, applied scientist | In person | Production projects, three concentrations |
| Artificial Intelligence, MS | Research and advanced technical work | AI research scientist, NLP engineer, computer vision specialist | In person | Faculty research labs, generative AI |
| Computer Science, MS | AI software engineering and systems | AI software engineer, ML infrastructure engineer, backend engineer | On campus or online | Java bridge course, NYC Design Factory |
| Data Science, MS | Data science and analytics | Data scientist, data analyst, ML analyst | On campus or online | Portfolio focus, Python/TensorFlow stack |
How to Move Into an AI Career in 5 Steps
The path to breaking into AI looks different for a working developer than it does for someone crossing over from another field. Still, most successful moves share the same handful of steps.
Step 1: Choose a specialization
The areas above reward different strengths, so start by matching one to your background and interests. A statistics-minded analyst may take to data science quickly, while someone who loves systems and scale is drawn to AI software engineering. Picking a direction early keeps your coursework, projects, and job search pointed at the same target.
| Your strengths/interests | Matching field | What to study at Pace |
|---|---|---|
| Finding patterns in data | Machine learning or data science | MS in Applied AI or MS in Data Science |
| Language and how meaning works | Natural language processing | MS in AI |
| Machines that move and see | Robotics or computer vision | MS in AI |
| Building systems that scale | AI software engineering | MS in Computer Science |
| Fairness, policy, and accountability | AI ethics and governance | MS in Applied AI (ethics core) |
| Many interests fit more than one path. Keep in mind that these are starting points, not the only routes. | ||
Step 2: Learn the fundamentals
Every AI specialty needs the same common base:
- Programming (usually Python)
- Statistics and linear algebra
- Working knowledge of how machine learning models are trained and evaluated
A graduate program builds these, though self-study and bootcamps can fill gaps for people who already work in tech. Career-changers without a computing background often start with a bridge course, which several Pace programs offer.
Step 3: Build real experience
Employers hire on evidence, and a portfolio of projects can outweigh a list of courses. Contribute to open-source tools, enter Kaggle competitions, or build an application end to end and put it where a hiring manager can see it. Graduate programs help here through capstone projects, faculty research, and industry partnerships that enable you to produce work you can actually show.
Step 4: Earn credentials that signal skill
Certifications from cloud and AI platform providers can strengthen a résumé, especially for career-changers who need to prove hands-on ability. A graduate degree is often required for advanced and research roles, many of which list a master’s as a baseline. The right education depends on where you want to go. Those looking to enter the field may only need a certificate to get started, while those looking to fill advanced AI roles will need a degree.
Step 5: Network where the work happens
Many AI roles are filled through referrals and professional connections before they ever appear on a job board. Attend meetups and conferences, join AI communities, and connect with people already doing the work you want. Location helps: being near a tech hub like New York City puts you close to the employers, alumni, and events that turn conversations into offers.
Advance Your AI Career at Pace
The AI field has room for many kinds of talent, and the surest way in is a graduate program matched to the work you want to do. Pace University’s Seidenberg School of Computer Science and Information Systems offers four routes into it:
Seidenberg is based in the heart of New York City, one of the world’s densest tech economies. Students can work in labs like the Pace AI Lab and the Robotics Lab, take on real projects through the NYC Design Factory, and connect with an alumni network of more than 161,000 people at companies from Google to OpenAI. That proximity is a major advantage, and it frequently helps Pace students turn their coursework into internships, and internships into job offers. The in-person programs put you in the middle of it, while the online options in computer science and data science open the same faculty and network to students anywhere.
Whichever path fits your goals, the next step is a short conversation. Request information to compare the programs, ask about admission and bridge courses, and find the one that will help you get where you want to go.
Frequently Asked Questions
Do you need a computer science degree to work in AI?
No, you do not always need a computer science degree to work in AI. Many AI roles value demonstrated skill, a strong project portfolio, and the right technical foundation over a specific undergraduate major. Career-changers from math, engineering, or other quantitative fields regularly move into AI, often by completing a graduate program with a bridge course that fills any gaps in programming or theory before the core coursework begins.
Is a master’s in AI worth it?
Yes, a master’s in AI is worth it, especially for advanced, research, and leadership roles. Many senior AI positions list a master’s as a baseline expectation, and a graduate degree can raise both your earning potential and the range of jobs open to you. For entry-level work, a strong portfolio may be enough, so the value of a degree grows with how far you want to advance.
What is the difference between an MS in AI and an MS in Computer Science?
The difference between an MS in AI and an MS in Computer Science is scope. A computer science master’s is broader, covering algorithms, systems, and software engineering, with AI as one focus area among several. An AI master’s concentrates on intelligent systems, machine learning, natural language processing, and computer vision. Choose computer science for flexibility across the tech field, or AI for depth in the science of intelligent systems.
How long does a master’s in AI take?
Most of Pace’s Seidenberg graduate programs typically require the completion of 30 to 34 credits and take about two years of full-time study. Part-time schedules extend that timeline but let working professionals stay employed while they study. Computer science and data science also offer online options, which give students flexibility in how quickly they move through the coursework.
Can you study AI online?
Yes, you can study AI online, at least in part. Pace offers its MS in Computer Science and MS in Data Science both on campus and fully online, with the same curriculum and faculty in each format. The MS in Applied Artificial Intelligence and MS in Artificial Intelligence are in-person programs, since both draw heavily on lab work and faculty-led research on the New York City campus.