AI and Machine Learning Jobs Global Hiring Trends 2025

I’ve always been curious about how AI and machine learning jobs vary around the world—not just in terms of what they pay, but how long it takes to grow a career, and whether things like a PhD really make a difference. So I decided to explore it myself.

I pulled together a dataset of over 15,000 job postings across 20 countries and 15 industries, and built a dashboard to dig into the patterns. What started as a side project quickly turned into something much bigger. The more I explored, the more interesting it got—especially when comparing Australia with other countries like the U.S.

One of the biggest things that stood out is just how much location matters. Salaries for AI roles can vary dramatically depending on where you are. In fact, the difference between the highest and lowest-paying regions can be more than sixfold. Northern Europe and North America lead the pack, while South Asia lags behind. Australia sits somewhere in the middle-upper range—not bad, but there’s definitely room to grow.

When I looked at industries, I expected Finance to be a top-paying sector everywhere. But in Australia, that wasn’t the case. In fact, U.S. AI salaries in Finance are almost 40% higher than in Australia. The only industry where Australia slightly edges out the U.S. is Transportation—but only by 0.13%. It really shows how industry choice can affect your earnings, even within the same field.

I also wanted to know: does education really boost your pay? Globally, people with Master’s degrees seem to earn the most in AI. But in Australia, it’s a bit different. PhDs often come out on top—but not always. In large companies, PhDs sometimes earn less than people with lower degrees at the same experience level. That was surprising. It made me rethink the assumption that more education always means more money.

Another thing I found: career progression takes longer in Australia. In many industries—like Manufacturing and Finance—Australian employers ask for more years of experience than their U.S. counterparts. In small companies, I even found executive roles that require up to 17.67 years of experience. That’s a long road, and it’s something to keep in mind if you’re building your career here.

To make all of this more actionable, I built a salary calculator into the dashboard. You can choose your industry, company size, and how many people you want to hire at each level—and it’ll estimate your team’s total payroll. For example, hiring 15 people across Entry, Mid, Senior, and Executive levels in a mid-sized transportation firm in Australia could cost around $2.1 million AUD a year. It’s a handy tool for anyone planning a team or pitching a hiring strategy.

*This post showcases the Tableau version of my dashboard. Please check out the Power BI version here: https://datawithyifan.com/aijobspowerbi/.
*For the best viewing experience, please use a desktop or laptop.