1 in 7 engineering jobs at 20 tech companies now has AI, ML or 'agent' in the title

Everyone says "every company is becoming an AI company". I wanted a number for that, so I looked at companies that are not AI labs: payments, dev tools, social and fintech. How many of their open jobs are AI jobs?

Disclosure: I built the Career Site Jobs API used to collect this data. It reads public job boards (Greenhouse, Ashby, Lever, Workday and more) and returns every open job as JSON.

A job counts as an "AI job" if its title contains AI, artificial intelligence, GenAI/generative, machine learning/ML, MLOps, LLM, or agent/agents/agentic. I only looked at titles, not descriptions, so these are roles where AI is the job itself, not a nice-to-have.

367 of 4,794 open jobs (7.7%) have one of those terms in the title.

Among engineering titles (engineer, developer or architect), the share is higher: 230 of 1,672 (13.8%), or about 1 in 7.

"LLM" in a title has almost disappeared. Companies write "AI" instead.

(The chart has all 18 companies with 40 or more open jobs. Dropbox had 23 open jobs and Airtable 4, so I left them out of the ranking.)

The two ad-driven social platforms lead by a clear margin. Ranking, recommendations and ad targeting have been machine-learning problems for a long time. 26 of Reddit's 32 and 28 of Pinterest's 30 AI titles are machine learning roles, many in ads, retrieval and relevance. Databricks has the most AI jobs in absolute terms (86), as expected for a data and AI platform.

At the bottom are the payments and fintech companies: Stripe, Robinhood, Ramp and Brex are all at 4–5%. They are hiring a lot (Stripe has 719 open jobs), but mostly for roles without AI in the title.

48 titles mention agents, 30 of them with the word "agentic". Examples straight from the boards:

By company: Databricks 15, Datadog 9, Cloudflare 8, GitLab 8, Robinhood 3, Coinbase 2, and one each at Stripe, Reddit and Figma. (A caveat: Datadog has a product called "the Datadog Agent", so a few of its matches are about that product, not AI agents.)

The topics are telling: commerce (agents that buy things), observability and security for agents, and agents in software delivery. Infrastructure companies are building the tooling around agents.

This was the clearest pattern in the data:

More than one in four AI jobs is at Staff level or above, more than twice the rate for other jobs. Companies seem to be hiring experienced people to set up AI work, not many juniors to scale it.

For job seekers that's mixed news. The openings exist, but the entry door is narrow. Pinterest is the exception: it lists several machine learning internships and university-grad roles for 2027.

On pay: only 269 of the 4,794 postings (5.6%) had a USD yearly salary range in the structured fields. Among engineering roles with one, the 11 AI-titled roles had a median midpoint of $272k and the 58 others $227k. Eleven is too few to conclude much, so treat that as a hint, not a finding.

The input for the Career Site Jobs API is just the company slugs:

{ "companies": [ "stripe", "databricks", "datadog", "cloudflare", "reddit", "pinterest" ], "maxJobsPerCompany": 0 } Enter fullscreen mode Exit fullscreen mode maxJobsPerCompany: 0 returns every open job. Each job comes back with title, department, location, remote flag, salary fields (when posted), date and URL, so the counting above is a few lines of Python with re. The tool costs $2 per 1,000 jobs, so the whole dataset here would cost about $9.60. If you only care about new AI roles, turn on onlyNewJobs and schedule it daily. Each run then returns only postings that weren't there the day before.