Google Jobs Scraper — a stable Google Jobs API for recruiters, researchers & AI agents

Turn any job search into clean, structured data: give this actor a query like "registered nurse jobs in dallas" and it returns the jobs Google surfaces as tidy records — title, company, location, posting date, salary, and direct apply links with the source platform — ready for your ATS, spreadsheet, or agent pipeline.

Built for one thing the popular Google Jobs actors are rated poorly for: a reliable, stable schema you can depend on.

What you get

• Clean, normalized records. Salary parsed into salaryMin / salaryMax / salaryCurrency / salaryType (e.g. $40K–$43K a year → 40000, 43000, USD, year), posting age turned into an ISO postedAt date, employment type, and a full job description.
• Direct apply links, de-Googled. Every listing's real apply URLs (employer ATS, LinkedIn, Indeed, ZipRecruiter, Greenhouse, Lever, Workday, …) — not Google redirect links — each tagged with the platform and domain where we can identify it.
• Multiple queries per run, optional location, countryCode, and a postedWithinDays freshness filter that maps to Google's own "Date posted" windows.
• A stable, documented output schema with dataset views (overview, salary, apply links, full) and a run-report record so you always know what happened.
• Pay-per-result pricing: you're charged per job record, nothing else.

Input

{
  "queries": [
    "registered nurse jobs in dallas",
    "software engineer jobs in seattle"
  ],
  "location": "",
  "countryCode": "us",
  "maxResultsPerQuery": 50,
  "postedWithinDays": 7
}

Only queries is required. Write queries the way you'd type them into Google. postedWithinDays accepts 1, 3, 7 or 31 (it rounds up to Google's nearest window). The default proxy is Apify's Unblocker group — see Limitations.

A sample record

Real record from a live capture (chef jobs in seattle, 2026-09-03) — every field exactly as Google served it:

{
  "jobId": "LQVJsKheF836EEshAAAAAA==",
  "title": "Full-Time Sous Chef — Culinary Leader in Seattle",
  "company": "Wolfgang Puck Catering",
  "location": "Seattle, WA",
  "via": "JobLeads",
  "postedAt": "2026-08-21",
  "postedRelative": "13 days ago",
  "employmentType": "Full-time",
  "salaryMin": 40000,
  "salaryMax": 43000,
  "salaryCurrency": "USD",
  "salaryType": "year",
  "salaryRaw": "$40K–$43K a year",
  "descriptionSnippet": "Wolfgang Puck Catering in Seattle is hiring immediately for a full-time Sous Chef to lead kitchen operations, supervise cooks and food-service staff, and ensure recipe and production standards are met.\nThe role is based at 2122 7th Avenue, Seattle, WA 98121 with schedule Monday through Friday (hours",
  "applyLinks": [
    { "url": "https://www.jobleads.com/us/job/full-time-sous-chef-culinary-leader-in-seattle--seattle--e056edee599e22a704dcc79c7e4aae156?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic", "platform": "JobLeads", "domain": "jobleads.com" },
    { "url": "https://jooble.org/jdp/-7824064096309794040?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic", "platform": "Jooble", "domain": "jooble.org" }
  ],
  "query": "chef jobs in seattle",
  "capturedAt": "2026-09-03T07:04:47Z"
}

The dataset also ends with a single run-report record (isRunReport: true) summarizing counts, per-query outcomes, and whether a time or budget limit was hit.

Typical uses

• Recruiters monitoring who's hiring for a role in a market, with apply links already resolved to the real ATS.
• Market & labor researchers pulling salary and employer data across roles and cities.
• AI agents that need a dependable jobs-data endpoint with a schema that doesn't shift under them.

For AI agents (MCP)

This actor is MCP-ready and pay-per-event: agents calling it through mcp.apify.com (Claude, Cursor, VS Code, ChatGPT) pay per result record via Apify prepaid credit or x402/USDC — no monthly plan.

• Input: queries (string array, required), optional location, countryCode, maxResultsPerQuery, postedWithinDays.
• Output: flat records with stable names and types — jobId, title, company, location, postedAt, salaryMin / salaryMax / salaryCurrency / salaryType, applyLinks[], descriptionSnippet, query, capturedAt — plus a final isRunReport record.
• Reliability: the schema is versioned and documented, and every run self-stops cleanly inside its time budget — no hung agent calls.

Limitations (read these)

Honesty first — this is a scraper of a surface Google actively protects:

• Anti-bot is an arms race. This actor reads the public Google Jobs surface (google.com, no login, no captcha-solving). Google changes its layout and defenses; when it does, output can degrade until the parser is updated (see MAINTENANCE.md). We design so one field breaking never breaks the whole run.
• Salary and apply links appear on only some listings. Google shows a salary on roughly a third of jobs and apply links on most but not all. When a field isn't shown, it's null — we never invent it. "Completeness" here means relative to what Google displays (measured at 100% of shown fields in testing), not that every field is filled on every job.
• Results per query. With the default Unblocker proxy (which reliably gets past Google's bot wall in the cloud), each query returns the top page of results Google serves (~10–15 of the most relevant jobs) — deep, clean, and dependable. To pull deeper result sets you can switch proxyConfiguration to the Residential group, which supports full pagination up to maxResultsPerQuery, but is more likely to be throttled by Google. For breadth, prefer several focused queries over one broad one.
• A supported proxy is required in the cloud. Google blocks datacenter IPs; the run needs an Apify plan that includes the Unblocker (default) or Residential proxy group.
• Memory. Minimum 1024 MB. If you switch to the Residential group for deep pagination, give the run at least 2048 MB — that path launches a browser to mint the session, which needs the headroom.

Keywords

Google Jobs, Google Jobs API, jobs API, job listings scraper, job search API, Google for Jobs, recruiting data, salary data, apply links, ATS leads, MCP, AI agents, agent tool, job listings API.