One CV says machine learning engineer. The other says senior data scientist. Underneath, the work is close to identical: Python, PyTorch, a model in production, some MLOps. Your recruiter tells you the first will cost forty percent more.
Before you sign that off, check which number you are being shown. The 30 to 50 percent divergence is real, but it is almost entirely a US equity story, and it does not currently show up in UK advertised salaries at all.
The US total compensation gap is real
On Levels.fyi, updated 10 August 2026, the median ML/AI software engineer in the United States reports $245,000 in total compensation. The median data scientist reports $180,000.
| US, total compensation | 25th percentile | Median | 90th percentile |
|---|---|---|---|
| ML/AI software engineer | $166,000 | $245,000 | $509,300 |
| Data scientist | $131,000 | $180,000 | $350,000 |
That is a 27 percent gap at the 25th percentile, 36 percent at the median and 46 percent at the 90th. So the headline holds, provided you are measuring total compensation, in the United States, from a self-reported sample.
Change any one of those three conditions and the gap collapses.
Now look at cash
Robert Half's 2026 salary guide puts the AI/ML engineer midpoint at $170,750 with 4.4 percent projected growth, and the data scientist midpoint at $153,750 with 4.1 percent growth, on a range of $121,750 to $182,500. That is an 11 percent premium, not 40.
Burtch Works reaches the same place by a different route. Its 2025 AI and Data Science Compensation Report, covering 866 professionals, finds AI professionals commanding a 9 to 13 percent cash premium over data scientists, widest where scarce generative AI expertise adds most value. Its data science mean base salaries run from $109,545 at IC-1 to $158,861 at IC-3.
Two independent US sources, two methods, roughly the same answer: about ten percent on cash.
So the divergence is not a salary divergence. It is an equity divergence, and it is concentrated in a small number of employers that grant a lot of stock. Levels.fyi data cited in Pin's 2026 benchmark review puts Anthropic's median total compensation at $600,000 and OpenAI's at $795,000 as of May 2026. Those figures pull the upper tail of the ML/AI distribution hard.
Worth noting: the top of the data science market is not poor either. Levels.fyi has Citadel at $533,750, Stripe at $478,000 and Netflix at $472,500 for data scientists. The difference is not that ML pays better at the top, it is that a larger share of ML roles sit near the top.
The UK does not show the divergence at all
ITJobsWatch tracks advertised permanent vacancies. For the six months to 10 August 2026:
| UK, advertised base salary | Median | Year on year | London median |
|---|---|---|---|
| Machine learning engineer | £76,000 | -10.59% | £90,000 |
| Data scientist | £75,000 | +7.14% | £80,000 |
| LLM as a listed skill | £80,000 | -11.11% | £95,000 |
| AI as a listed skill | £71,000 | +2.53% | £85,000 |
The national gap between the two titles is £1,000, or 1.3 percent. In London it is £10,000, or 12.5 percent, which is close to the US cash premium and nothing like the headline.
The direction of travel also runs against the story. ML engineer medians are down 10.59 percent from £85,000, LLM roles are down 11.11 percent from £90,000, and data scientist medians are up 7.14 percent from £70,000.
Be careful with that first figure. The UK ML engineer median rests on 36 quoted salaries across 65 vacancies, against 376 quoted salaries for data scientist. A double digit fall on a sample that small is not a crash. It is a thin, formerly overheated segment that has stopped being bid up.
On total compensation, Levels.fyi puts the UK data scientist median at £81,673, with the middle 50 percent between £56,231 and £109,090 and London at £98,043. That sits only modestly above the £75,000 advertised base, which tells you the equity wedge driving the US gap barely exists for most UK employers. Where it does exist, it is at US-headquartered firms: JPMorgan Chase at £144,733, Meta at £144,026, Google at £136,350.
Why the published sources disagree
Four reasons, and only some of them are noise.
Total compensation versus cash. Levels.fyi reports total compensation. Robert Half and ITJobsWatch report cash. The same market yields a 36 percent gap or an 11 percent gap depending purely on that choice.
Who volunteers. Levels.fyi is self-reported and skews towards large technology employers and towards people who negotiated well enough to want to publish the result. ITJobsWatch is a census of advertised vacancies, including insurers, the public sector, consultancies and agencies. Neither is wrong. They describe different populations.
The titles have converged. 50.64 percent of London data scientist adverts now require machine learning and 48.94 percent require AI. You are often benchmarking two labels for one job, and the label carries a premium the work does not.
Reference dates. The BLS still reports a US data scientist median of $112,590 from May 2024. It is the most rigorous national figure available and it is two years stale in a market that repriced twice in that window. Do not anchor on it, and be wary of any guide that quietly does.
One more piece of context: demand is broadening, not narrowing. Indeed Hiring Lab recorded AI mentions at a record 4.2 percent of US postings in December 2025, and in the UK, AI now appears in 9,161 permanent IT vacancies, 8.65 percent of the market, having climbed from 41st to 7th in the skills ranking in two years. Broadening demand compresses a specialist premium, it does not widen it. Levels.fyi's own 2025 annual report puts US software engineering median growth at 3.49 percent and data science at 2.92 percent. A gap of half a percentage point a year is not a divergence.
Setting a band this quarter
Split the band by compensation element before you split it by title.
Set your cash band from cash sources. For a mid-level US hire, that is roughly $154,000 to $171,000 across the two titles, with a 10 to 15 percent uplift where the role genuinely owns production ML. For a London hire, £80,000 to £90,000 on the same logic.
Then decide separately and explicitly whether you are competing for the equity-rich tail. If you cannot grant meaningful stock, the 30 to 50 percent figure is not your benchmark. Matching it in cash will overpay against your actual competitive set and still lose the candidate weighing an offer from a frontier lab.
And test the premium against the job description rather than the title. If the role does not own deployment, monitoring and inference cost, you are paying an AI/ML premium for a data science job.
If you want your band stress tested against live offer data for your sector and location, talk to Synerjy.