Launch your career in AI: Top jobs, salaries, and skills for 2026
Learn what it takes to break into careers in AI, including top roles, AI jobs salary data, and required skills and education.
AI careers are booming — here’s why
Investment in artificial intelligence (AI), automation, and machine learning continues to grow, and hiring is expanding with it. Technical jobs in AI now span industries such as healthcare, finance, and logistics as organizations build systems and business models powered by data and automation.
The Fall 2025 edX AI Learning Hesitation Gap survey reflects this momentum. Seventy-nine percent of U.S. adults say they're interested in learning AI skills, yet 26% say they don't know where to start. Interest is high, but many professionals are still figuring out how to enter the field.
"The hottest job in 2026 right now is anything that has to do with AI because that's the buzzword," says Holly Lee, a former recruiting leader at Amazon, Google, Meta, and Microsoft. Lee emphasizes that employers are hiring for roles that require hands-on skills in building, testing, and deploying AI systems.
Here's what to expect in today's AI job market.
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Start, switch, or advance your AI career
To work in AI, depending on your role, you will probably need to know how to write and debug code — most commonly in Python — work with datasets, and train, evaluate, and improve machine learning models. Many professionals build these skills through a bachelor's or master's degree in computer science, software engineering, data science, or engineering, but a formal degree isn't always required.
If you already have experience in software development, statistics, or data analytics, you can transition into entry-level AI roles by strengthening your machine learning knowledge and building a portfolio of real projects that demonstrate applied skills.
Career starters without prior experience often take longer, especially if pursuing a four-year degree. What matters most, says Lee, is demonstrating practical experience building and working with AI systems.
For career starters
While there's more than one way to get started, here's one path to start building technical skills from scratch:
- Start with AI introductory courses, such as Harvard's CS50x: Introduction to Computer Science. You'll learn core programming concepts, algorithms, data structures, and web development, which are essential for working in AI and machine learning.
- Consider a bachelor's degree in computer science, data science, or engineering to build a solid foundation in software development, statistics, and machine learning.
- Gain hands-on experience through internships, entry-level IT jobs, hackathons, or starter projects on GitHub. These practical experiences can help you apply your knowledge and build a shareable portfolio.
For career starters
While there's more than one way to get started, here's one path to start building technical skills from scratch:
- Start with AI introductory courses, such as Harvard's CS50x: Introduction to Computer Science. You'll learn core programming concepts, algorithms, data structures, and web development, which are essential for working in AI and machine learning.
- Consider a bachelor's degree in computer science, data science, or engineering to build a solid foundation in software development, statistics, and machine learning.
- Gain hands-on experience through internships, entry-level IT jobs, hackathons, or starter projects on GitHub. These practical experiences can help you apply your knowledge and build a shareable portfolio.
Professional spotlight: Meet Omar Jiménez
Profession:
Associate Data Scientist / Machine Learning Engineer at Xtillion
Education:
- Bachelor’s degree in engineering with a minor in applied mathematics, University of Puerto Rico at Mayagüez
- Master’s degree in computer science and engineering with a specialization in AI and machine learning, Georgia Tech
As an engineering graduate, Omar says he entered the machine learning field through a nontraditional path. Internships and undergraduate coursework sparked his interest in AI, which led him to pursue a master’s degree focused on AI and machine learning.
He now works as a data scientist and machine learning engineer at Xtillion, a consultancy firm focused on data and AI solutions for healthcare, banking, and the public sector.
During his tenure at Xtillion, he has upskilled by earning certifications, such as the Amazon Web Services Machine Learning Certification, to further strengthen his AI qualifications.
These experiences have reinforced his belief in AI's transformative power, not just in business but also in science, medicine, and society at large.
Here’s what Omar has to say about building a career in AI:
"I think it’s clear that AI is ushering in a new industrial revolution. I can’t think of any industry that won’t be affected.
There are many under-discussed areas, like AI for science, where we’re using AI to tackle fundamental challenges such as climate change. For example, DeepMind’s AlphaFold can predict the structure of any protein in the world and has driven massive advances in medicine.
There are countless opportunities to use AI to make a positive impact. It’s a challenging, ever-evolving field, but if you enjoy building smart systems, solving hard problems, and seeing real-world results, it’s definitely worth it."
What skills do you need to work in AI?
Most AI roles require the ability to write production-level code, train and evaluate machine learning models, and deploy systems at scale. "You have to be good at math; there's no way around that," Lee says. "You'll be dealing with numbers, data patterns, structures, and algorithms."
Beyond that foundation, job descriptions for AI jobs typically include as requirements knowledge and experience in technical skills such as:
- Programming languages: Python is the most common, as well as Java, R, and C/C++
- Machine learning frameworks: TensorFlow, PyTorch, or scikit-learn
- Data querying and manipulation: SQL, data cleaning, and feature engineering
- Statistical analysis and model evaluation: regression, classification, probability, and performance metrics
- Data visualization and BI tools: Tableau, Power BI, Looker, or matplotlib
- Cloud platforms for deployment: AWS, Azure, or Google Cloud
Top technical AI jobs and how to qualify
Lee highlights the following five technical roles as critical for the AI era. Learn about each role, with information on responsibilities, salary data, job growth, and common education requirements from the BLS:
Top-paying states for AI careers
Use this interactive map to discover the top-paying states for AI careers, then explore salaries by specific roles in all 50 states below.
Take the next step in your AI career
AI is transforming every industry, and professionals with the right skills are leading that charge. Whether you're starting out, making a switch, or leveling up, edX offers flexible, high-impact learning designed to help you thrive.
Take the next step with:
- Beginner-friendly AI courses to build foundational skills
- Career-focused AI professional certificates with hands-on training
- Harvard's CS50 programs to build foundational computer science skills for AI
- Master's degrees in artificial intelligence from top universities
- Artificial intelligence executive education for experienced professionals
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