The tools AI job posts ask for, by role

Each month a collector reads the public job boards of a fixed panel of 100 employers that build or sell AI: model labs, AI-native applications, AI infrastructure companies, and established tech, data and fintech companies. It reads Ashby, Greenhouse and Lever boards through their public APIs and keeps only the posting ID, title, link, date and the terms each posting names.

A posting counts for one of eight roles by its title, tried in a fixed order: forward deployed engineer, GTM engineer, solutions engineer, AI engineer, RevOps, SDR or BDR, account executive and customer success. Managers, directors, heads, interns and recruiters are left out. Customer success managers, account managers and account directors count, because at AI companies these are individual seats.

Engineering roles count at every panel employer. Sales, RevOps and customer success roles count only at the AI companies on the panel, so the sales picture describes selling AI. Postings from one employer with the same title and description count once, whatever their locations.

The dictionary has 277 terms in three kinds. Tools are named products you can learn and put on a resume, such as Salesforce, Clay or Claude Code. Foundations are what most postings in a field assume, such as Python, SQL, AWS or Excel. Practices are the work itself, such as building agents, running evals or a sales methodology. Ambiguous names count only in context: Conductor only beside coding agents, Segment only as the data product and Go only as a language.

A term counts once per posting. The collector reads each posting's headings and notes where the term appears: in the requirements, in the responsibilities, in a nice-to-have list or somewhere else. A sentence that calls a skill a plus, a bonus or preferred counts as nice to have wherever it sits. When a posting names a term in more than one place, the strongest place counts: required first, then responsibilities, then nice to have. A term in the job title counts as required.

An employer's own products never count for that employer. Names in a list of customers, investors or partners do not count, and neither does any line an employer repeats across at least 50% of its postings, which is where about-us, benefits and legal text sits.

The headline percentage is the share of employers hiring for a role that name the term in at least one posting, so an employer posting the same job fifty times counts once. A tool needs at least two employers to be ranked. Share is the fraction of a role's postings that name the term, shown when you open a row. Capped share counts at most the 20 most recent postings from each employer, so one company hiring in bulk cannot set the order. Lift compares a role's share with the share across all eight roles together: a lift of 3 means the role names the term three times as often as the average posting. A signature tool has a lift of at least 2, a share of at least 6% and at least 3 employers.

Emerging marks an AI-native product a person works in or builds agents with, released or widely adopted since 2023: coding agents, AI work assistants, AI-native sales and automation tools, and the newest agent SDKs. The radar lists emerging tools that at least two employers name for a role, however small the share.

A role with 40 or more postings is ranked. A role with 20 to 39 is shown with a small-sample note. A role with fewer is not shown.