SignalsAPI AI-Engineering Demand Cut

AI and machine-learning roles are advertised alongside — not instead of — the data and platform work that puts them into production, so demand for the surrounding specialisms moves with them rather than against them.

Demand by cut

Terms are matched against a posting's job title, case-insensitively and on a word boundary — so “llm” matches “LLM Engineer” and not “fulfillment”. They are not matched against the description of the posting: the corpus surface these pages are built from carries a posting's title, company, location and salary but not its body text, so a posting whose body mentions a term under an unrelated title is not counted. Every list below therefore understates its term's true incidence, in the same direction and for the same reason. Each list was frozen before this edition was cut — see the pre-registered term lists for the dates and why each term is on it.

AI & machine learning by industry

Software & IT leads: 1.43% of the postings advertised in it name an AI or ML title.

Each sector's figure is the share of that sector's own advertised postings, so the sectors are comparable to each other even though we hold an industry label for only some employers. It is not a share of all postings, and it is not a count.

Industry groups are matched against the industry label the source itself publishes for the employer, case-insensitively and as a substring — so “software” matches “Software Development”. Substring matching is looser than the word-boundary rule used for job titles, which is why every term below is a phrase specific enough that no unrelated label contains it. A label may match more than one group; each group is measured against its own postings, so an employer counted in two groups is counted correctly in both rather than twice in one.

Industry labelling covers 67.22% of the deduplicated postings these pages render (measured 2026-07-22). That coverage is not evenly distributed: it is 88.81% on our largest single source and 31.55% across every other source combined, and two national corpora carry almost no industry labels at all. So counting postings per industry and ranking the industries would largely rank which sectors our best-labelled source happens to cover. Any figure we publish by industry is therefore a rate measured inside a sector — the same labelled population in the numerator and the denominator — never a share of the whole corpus, and it excludes every posting whose employer carries no industry label.

The aggregate file carries every figure above. The row-level file carries the postings they were counted from, and states per cut whether it lists all of them or the most recent sample. Attribution required.

Live hiring pages behind this study

Live hiring pages cut from the same corpus, refreshed every three hours — this study is the monthly print of it.