Track hiring signals across a list of companies

Open roles, the functions being hired, and the careers page URL: a decent proxy for where a company is investing.

Updated August 2026

What goes in, what comes out

Input

  • Company
  • Domain

Output

  • Open roles
  • Top hiring function
  • Careers page URL
  • Hiring signal

The prompt

Paste this into the description field, or write your own version of it.

Find the approximate number of currently open job postings, which function they are concentrated in, the careers page URL, and whether hiring appears to be expanding or slowing.

Why this works

Careers pages are public and job boards are indexed. Hiring is one of the more honest signals a company emits about its priorities, far more so than its marketing copy.

Getting the best results

Hiring moves daily, so the direction of travel is the signal worth tracking rather than any single count. Re-run monthly and you get a trend line that is genuinely predictive of where a company is investing.

What it costs

On Gemini 3.1 Flash-Lite, a typical row of this shape costs about <$0.01, so roughly $0.27 per thousand rows, paid directly to Google. OpenClay adds nothing.

The tool shows an exact figure after a five-row test, before you commit to the batch. See all models and pricing.

Step by step

  1. 1Load target accounts. A company name column is enough; a domain improves precision.
  2. 2Ask for the signal, not just the count. Direction of hiring is more useful than a raw number.
  3. 3Test and run. Verify on five rows, then run the batch.
  4. 4Re-run on a schedule. Hiring data ages quickly, re-enrich monthly for a trend.

No account, no card, no platform fee.

Bring your own API key and pay the model provider directly.

Start enriching, free

Other use cases

  • Enrich a company list with AI: Turn a column of company names into leadership, funding, headcount and positioning, researched from the live web one row at a time.
  • Enrich a lead list without paying for Clay: Export raw prospects from Apollo, lemlist or LinkedIn, enrich them with live research, and pipe them back into your sequencer.
  • Build a competitor matrix with AI: Give it a column of competitors and get pricing, positioning, target segment and differentiators back as structured columns.
  • Enrich a list of universities: Rankings, acceptance rates, tuition and programme details for hundreds of institutions, without copying from a dozen tabs.
  • Enrich any CSV with AI: The generic case. If you can describe the column you want in a sentence, and the answer exists on the public web, this works.