AI engineers tend to out-earn data scientists, and the gap is widest in the UK and the US, where AI engineering has become the higher-paid specialism. The two roles overlap heavily, so the comparison is really between the work each does rather than the job titles, and pay follows the work. All the figures here are self-reported survey data, because job listings rarely state pay.
The short version is that the more a role leans toward building and shipping AI systems, the more it pays, and AI engineer titles lean that way more than data scientist ones. Neither is a poorly paid career, so the choice is better made on the work than on the number.
AI engineer vs data scientist: which pays more?
AI engineers pay more than data scientists on average, with the difference ranging from small at the global level to large in the UK, according to developers reporting their own pay.
Stack Overflow’s 2025 survey lets the two roles be compared directly across markets.
Globally the two are close, at roughly 89,000 US dollars for AI/ML engineers against 83,000 for data scientists. The gap widens sharply in the UK, where AI/ML engineers report about 150,000 against 74,000 for data scientists, and it holds in the US at 190,000 against 145,000. Where the market is hottest, the build-side role pulls ahead.
Why does the AI engineer role pay more?
The AI engineer role pays more because it sits closer to shipping production systems, and the market currently rewards that skill set most.
Data science grew up around analysis, experimentation, and insight, while AI engineering centres on putting models into products that run at scale, and production work has historically commanded a premium over analytical work in software generally. The premium tracks that difference. Lightcast, in its Beyond the Buzz analysis, found a 28% salary premium for AI skills, rising to 43% for two or more, and AI engineer roles tend to stack more of those applied skills than data scientist roles do. The pay gap is really a skills-mix gap wearing two different job titles.
Are the two roles even that different?
The two roles are less different than the titles suggest, because employers apply both loosely and the day-to-day work often overlaps.
Plenty of data scientist roles are really AI engineering, and some AI engineer roles are mostly analysis. Reading the tools a listing names tells you more than the title does. A role built around retrieval, agents, and production services is AI engineering whatever it is called, and it will tend to pay toward the higher end. A role built around statistics, experimentation, and reporting is data science, and it will tend to pay toward the lower end of this comparison, though a senior data scientist at a strong company can out-earn a junior AI engineer easily.
| Dimension | AI/ML engineer | Data scientist |
|---|---|---|
| Core of the job | Building and shipping model-powered systems | Analysis, experiments, insight |
| Typical tools | LLMs, RAG, agents, production services | Statistics, notebooks, SQL, ML libraries |
| Median pay, UK | around 150,000 USD | around 74,000 USD |
| Median pay, global | around 89,000 USD | around 83,000 USD |
Which role is in higher demand?
AI engineering is in higher demand than data science on current evidence, both in the pay premium it commands and the volume of roles.
The demand shows up in listings. In our analysis of live European AI roles, engineering positions numbered 179 against 59 in data science and research, so the build-side roles are several times more common. That imbalance is part of why they pay more: scarcity of supply relative to demand pushes the price up, and the applied AI skills these roles need are still catching up to what employers want.
Demand and pay reinforce each other here. When a role is both scarcer and harder to fill, employers raise the offer to compete, which is exactly the position AI engineering is in relative to data science right now. Data science is not a declining field, but it is a more mature one with a larger trained supply, so the same upward pressure on pay is weaker.
Which should you aim for?
The role to aim for is the one whose work you would rather do, then steered toward the higher-paying, more applied end.
If you enjoy analysis and drawing conclusions from data, data science is the natural home, and adding applied AI skills lifts both your pay and your options. If you enjoy building systems that run, AI engineering pays more today and has more openings. Because the two overlap, the highest-impact move for either is to add the applied AI skills, retrieval, agents, and evaluation, that the market pays a premium for. That choice matters more for your earnings than which of the two titles you put on your CV.
The direction of the market reinforces the point. The World Economic Forum’s Future of Jobs Report 2025 ranks AI and machine learning specialists among the fastest-growing roles of the decade, so both paths sit on a rising tide. The question is less which title is safer and more which kind of work you want to do for the next decade, since both will be in demand and both reward the same applied skills.
Compare live roles directly in remote AI engineer jobs and data science and AI research, with machine learning roles and every AI role open across Europe for the wider view.