Greenhouse Job Scraper — AI & ML Companies

For AI-talent recruiters, industry trackers, and comp-research pipelines who need Greenhouse job data at scale from AI/ML-native companies — scraping Anthropic, Scale AI, Databricks, and more in one run.

Pay per result — $1.50 / 1,000 jobs. First jobs free on the Apify free plan.

Scrape job postings from any Greenhouse-powered company board via the official public API. Give it a list of company slugs — get back clean, enriched job data in seconds. No API key. No login. No proxy. No browser. Runs in Apify cloud.

Get started instantly: use the pre-filled AI-native examples (anthropic, scaleai, databricks) or swap in any other Greenhouse company slug. Try it free — Apify's $5/month free tier covers thousands of jobs.

What this Greenhouse scraper does

• Fetches every open job from any company that uses Greenhouse ATS, using the official public endpoint.
• Returns a clean flat schema — identical fields every run, no parsing surprises.
• Enriches each job with seniority (intern / entry / mid / senior / lead / staff / principal / manager / director / vp / executive) inferred from the job title.
• Detects remote type (remote / hybrid) from location text.
• Returns full job descriptions as both plain text and HTML (togglable).
• Includes a parse_confidence score (0–1.0) and warnings list in every record — you see exactly how clean the data is.
• Supports multi-company batch runs (scrape 100 companies in one run).
• Filters by title keyword, location keyword, or remote-only.
• maxJobsPerCompany cap (default 50) prevents surprise bills on first runs.

How to find a Greenhouse slug

Look at the company's careers page URL:

• job-boards.greenhouse.io/anthropic → slug is anthropic
• job-boards.greenhouse.io/scaleai → slug is scaleai
• boards.greenhouse.io/databricks → slug is databricks

Many top AI and tech companies use Greenhouse to power their job boards.

What data you get

One flat row per job — 15 structured fields:

{
  "title": "Member of Technical Staff",
  "company": "anthropic",
  "location": "San Francisco, CA",
  "remote_type": null,
  "seniority": "mid",
  "salary": null,
  "department": "Engineering",
  "employment_type": null,
  "posted_at": "2026-05-20T16:58:18-04:00",
  "url": "https://anthropic.com/jobs/search?gh_jid=1234567",
  "apply_url": "https://anthropic.com/jobs/search?gh_jid=1234567",
  "job_id": "1234567",
  "global_id": "greenhouse:anthropic:1234567",
  "description_text": "Anthropic is looking for a Member of Technical Staff...",
  "description_html": "<p>Anthropic is looking for...</p>",
  "parse_confidence": 1.0,
  "warnings": [],
  "scraped_at": "2026-05-31T12:00:00+00:00"
}

Field notes:

• salary and employment_type are null — the Greenhouse public boards API does not expose these fields. For salary data, the company must publish it voluntarily in the description.
• parse_confidence is 1.0 for a fully-populated job; small deductions for missing optional fields. Check warnings for the reason codes.
• global_id format: greenhouse:{slug}:{job_id} — stable identifier for deduplication across runs.

Input schema

Field	Type	Default	Description
companies	string array	[anthropic, scaleai, databricks]	Greenhouse company slugs to scrape
titleKeyword	string	—	Case-insensitive title filter
locationKeyword	string	—	Case-insensitive location filter
remoteOnly	boolean	false	Return only remote jobs
maxJobsPerCompany	integer	50	Cap per company (0 = unlimited)
includeDescriptions	boolean	true	Include description_text + description_html

Pricing

Pay-per-result: $1.50 / 1,000 jobs ($0.0015 each). You pay only for jobs returned.

Worked example: 3 companies × 50 jobs = 150 results = $0.23. 10 companies × 200 jobs = 2,000 results = $3.00.

FAQ

Do I need an API key or proxy? No. The Greenhouse API endpoints used are publicly accessible — no auth, no proxy, no browser.

What output formats are available? JSON, JSONL, CSV, and Excel via the Apify dataset export, plus the Apify REST API.

Can I run it on a schedule? Yes — use Apify Scheduler. Combine with your own dedup on global_id (greenhouse:<slug>:<job_id>) to track only new postings.

What if a company slug is wrong or returns 0 jobs? The actor logs a warning, records the slug in failedCompanies, and continues scraping other targets — it never crashes on a single bad slug.

Reliability

The Greenhouse boards API is a stable, officially public endpoint with no authentication, no rate limiting, and no HTML parsing. This actor uses zero browser, zero proxy — just direct HTTP to an official JSON API. Parse confidence is consistently 1.0 on well-populated boards.

Typical run: 500 jobs for one company in under 5 seconds.

Use cases

• Hiring intelligence — track AI headcount growth, new research departments, and modeling roles.
• Job aggregation — build a searchable careers board for AI and ML companies.
• AI agent input — feed job data to an LLM agent for matching, summarizing or alerting.
• Lead generation — identify AI companies actively hiring in specific functions.
• Competitive analysis — monitor competitor hiring signals in real time.

Alternatives and comparison

Several Greenhouse scrapers exist on Apify Store. This one differentiates by:

Feature	This scraper	greenhouse-job-scraper (epctex)	i-scraper
parse_confidence score	Yes	No	No
Seniority enrichment	Yes (11 levels)	No	Partial
Remote type detection	Yes	Rarely	Rarely
Multi-company batch	Yes	Limited	Yes
Descriptions (text + HTML)	Yes	Some	Some
global_id for dedup	Yes	No	No
Price	$1.50/1k	varies	$1.90/1k

parse_confidence (0.0–1.0) and warnings are in every record — deductions for missing job_id (0.15), title (0.15), url (0.10), posted_at (0.05), description (0.05). Your pipeline sees data quality signals before it sees broken output.

Use with AI agents (MCP)

This actor is MCP-compatible. Use it as a data source in n8n, Make, or any LLM agent pipeline.

https://mcp.apify.com/?tools=bovi/greenhouse-job-scraper-ai-companies

The global_id field (greenhouse:<slug>:<job_id>) is a stable cross-run key. Feed this actor's output directly to vector databases, job-matching LLMs, or n8n/Make workflows. Use with the flagship Greenhouse, Lever & Ashby Jobs API to cover all ATS platforms in one pipeline.

Integrations

Built for hiring-intel and labor-market analysts tracking open roles across Greenhouse-powered companies at scale — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Not affiliated with Greenhouse

This actor uses the public Greenhouse Boards API, which is freely accessible to anyone. It is not affiliated with, endorsed by, or partnered with Greenhouse or its parent company Cornerstone OnDemand.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Greenhouse, Lever & Ashby Job Scraper
• Jobstreet Jobs Scraper
• Lever Job Scraper
• Naukri Jobs Scraper
• Seek Jobs Scraper
• Stepstone Jobs Scraper

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.