If you’re thinking about getting into AI, you’ve probably heard the same advice everywhere: learn math and computer science. That’s true, but it’s also incomplete. I’ve been working in software engineering long enough to see how the industry really hires, and the gap between what schools teach and what companies actually need is wider than most career guides admit.
Let me be honest. I’ve interviewed dozens of candidates for AI-related roles over the years. The ones who get hired aren’t just the ones with the highest GPA in calculus. They’re the ones who understand how to apply theory to real problems, who’ve built things, and who can explain their thinking clearly. That’s the part of the puzzle most people miss.
The Math and CS Foundation Actually Matters
Yes, mathematics is non-negotiable for AI work. But here’s the thing: you don’t need to be a pure mathematician. You need to understand linear algebra, statistics, and calculus well enough to read papers, debug models, and reason about algorithms. That’s different from solving abstract proofs for their own sake.
Linear algebra especially. If you’re building anything with neural networks, you’re working with matrices. If you don’t have solid intuition about how linear transformations work, you’ll spend months confused about why your model behaves the way it does. I learned this the hard way early in my career.
Computer science fundamentals are equally important. Data structures, algorithms, system design—these aren’t optional. When you’re training models on massive datasets or deploying them to production, you need to understand complexity, memory usage, and how to write efficient code. Companies hiring for AI roles expect you to know this stuff cold.
The mistake I see students make is treating math and CS as separate subjects. They’re not. They’re tools that work together. You need both.
Beyond the Degree: What Actually Gets You Hired
Here’s what I’ve learned from hiring and working with talented engineers: your degree opens doors, but projects close them. Meaning, your degree gets your resume read. Your projects get you the job.
Building things matters. It shows you can take theory and make it work. It shows you’ve hit problems and solved them. On my own path, I spent time building recommendation systems, working with image recognition, and deploying models to production. Those weren’t assignments. They were side projects I chose because I was curious. When I talked about them in interviews, I could speak from experience.
Companies in 2026 will care about this even more than they do now. The field is moving faster. Frameworks change. New research comes out constantly. What they really need are people who can learn independently, experiment, and ship working code. If you’ve done that before, you’ve proven you can do it again.
A few concrete things that help:
- Contributing to open-source AI projects shows real-world thinking
- Building a personal project using popular frameworks like PyTorch or TensorFlow
- Writing about what you learned helps you understand it deeper
- Competing in Kaggle competitions or similar challenges proves you can solve actual problems
The Specializations That Pay
If you’re asking about money—and let’s be honest, compensation matters—certain areas within AI pay better than others. Machine learning systems engineering tends to pay well because it’s rarer to find people who truly understand both the math and the engineering. NLP and computer vision are hot right now. Reinforcement learning is still relatively specialized, which means higher pay.
But here’s my honest take: choose a specialization because you find it interesting, not because you heard it pays well. You’ll spend too much time learning to fake genuine curiosity. The people who do well in this field are the ones who actually care about solving the problems they’re working on. That care translates into better work, which translates into better opportunities.
When I started, I didn’t choose my specializations based on salary surveys. I chose them because I was genuinely fascinated. That’s what got me through the hard parts of learning, and it’s what made me useful to employers.
What Hasn’t Changed
Some fundamentals remain constant regardless of what year you’re planning to enter the job market. Communication skills matter more than most engineers realize. You need to explain complex ideas simply. You need to write clear documentation. You need to work with people who aren’t engineers.
Curiosity matters. The field moves so fast that the specific tools you learn matter less than your ability to pick up new ones quickly. Show that you’re genuinely interested in understanding how things work.
Persistence matters. You’ll get stuck. Models won’t train. Code will break in ways that make no sense. The people who succeed are the ones who push through that frustration instead of giving up.
If you’re genuinely interested in AI jobs, get the fundamentals right—the math, the CS, the practical skills. Then start building. Start shipping. Start learning from real problems instead of textbooks. That’s what actually moves the needle.
Source: Top majors and courses for high-paying AI jobs in 2026
Laura Bennett
Tom Harrington