Monster Job Scraper — Salary, Apply URLs & Full Descriptions

Turn every live Monster.com job posting into clean, structured data — in seconds, at scale.

Scrape job listings from Monster.com, one of the largest U.S. job boards, and get back everything that matters: structured salary (min / max / currency / period), apply URLs, geo-coordinates, skills, extracted contacts and the full description. Search by keyword and location or paste a Monster search URL — no code required. Export to JSON, CSV, Excel or the API, or pipe it straight into Google Sheets, n8n, Make or an AI agent (MCP).

Why this scraper

• ⚡ Fast & low-cost — a lean HTTP data path, not slow page-by-page rendering, so large runs stay cheap.
• 🧾 Rich, typed records — 30+ structured fields per job, not raw HTML.
• ♻️ Cheap to monitor — incremental mode re-scrapes only what changed (see below).
• 🔔 Notifications built in — Telegram, Slack, Discord, WhatsApp or any webhook.
• 🤖 AI- & API-ready — compact output, MCP-friendly, one-click integrations.

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✨ Key features

• 🔎 Search or URL scraping — run a keyword + location search, or paste a Monster.com search URL you already ran in your browser.
• 💰 Structured salary — not just the raw text: salaryMin, salaryMax, salaryCurrency and pay period broken out for instant benchmarking.
• 🗺️ Geo-tagged — city / state / ZIP plus latitude & longitude and a remote flag, ready for mapping and regional analysis.
• 🧠 Full job details — description as text / HTML / Markdown, skills, employment type, occupational (SOC) category, posting date and the direct apply URL.
• 📇 Extracted contacts — emails, phone numbers and links pulled from each description for lead generation.
• ♻️ Incremental monitoring — schedule a run and get only what changed (NEW / UPDATED / EXPIRED), with reposted-job detection; unchanged jobs are skipped before their page is fetched.
• 🔔 Notifications — push new jobs to Telegram, Slack, Discord, WhatsApp or any webhook (n8n / Make / Zapier).
• 🤖 AI-ready — compact mode + drop-empty-fields mode keep payloads lean for LLMs and MCP.

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📤 Example output

{
  "id": "e6b35595-ceeb-4245-b571-76bada4f2b57",
  "title": "Neuro ICU Registered Nurse (NIGHTS) - $12K BONUS",
  "company": "Jobot",
  "location": "Dallas, TX",
  "city": "Dallas",
  "state": "TX",
  "postalCode": "75201",
  "country": "US",
  "latitude": 32.783,
  "longitude": -96.807,
  "isRemote": false,
  "salaryText": "$85,000 - $125,000 / Year",
  "salaryMin": 85000,
  "salaryMax": 125000,
  "salaryCurrency": "USD",
  "salaryUnit": "YEAR",
  "employmentType": "FULL_TIME",
  "occupationalCategory": "29-1141.00: Registered Nurses",
  "skills": ["Registered Nurse (RN)", "Neuro-Intensive Care Unit", "Critical Care"],
  "url": "https://www.monster.com/job-openings/neuro-icu-registered-nurse-...",
  "applyUrl": "https://jobot.com/apply/...",
  "extractedEmails": ["careers@jobot.com"],
  "extractedPhones": ["+1-214-555-0142"],
  "postedDate": "2026-07-28T13:49:47.165Z",
  "dateRecency": "6 days ago",
  "description": "TOP Shift Diffs * $12K BONUS * RELO ASSISTANCE ...",
  "source": "monster.com",
  "scrapedAt": "2026-08-04T21:33:37Z"
}

Descriptions are also available as HTML and Markdown, not just plain text.

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📚 What data can you extract?

• Core — id, title, company, location / city / state / ZIP / country, latitude & longitude, remote flag, posting date and recency.
• Salary — raw text plus structured salaryMin / salaryMax / salaryCurrency and pay period.
• Details (with Fetch full details) — description (text / HTML / Markdown), skills, employment type, occupational (SOC) category, apply URL and apply type.
• Contacts — emails, phone numbers and links extracted from the description.
• Incremental — changeType, first/last-seen timestamps and repost detection on scheduled runs.

Every field is present in standard mode (missing values are null); compact mode returns the core fields only, for lean AI/MCP payloads.

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⚙️ Input

Configure it in the visual editor — no code needed — or pass JSON via the API.

Field	What it does
query	Job title, skill or company; comma-separate several searches.
location	City, state or ZIP (e.g. New York, NY, Texas, 90210). Empty = nationwide.
radius	Miles around the location.
startUrls	Paste Monster.com search-result URLs to scrape directly.
employmentType / datePosted / companyName	Narrow the search.
includeDetails	Fetch each job's page for the full description, skills and contacts.
incrementalMode	Emit only what changed since the last run.
maxResults	Cap the number of jobs (0 = unlimited).
proxyConfiguration	Optional — works without a proxy at typical volumes.

…and 28 inputs in total — the table shows the essentials; the rest cover description formatting (descriptionFormat, descriptionMaxLength), AI/compact output (compact, excludeEmptyFields), notification channels (Telegram / Slack / Discord / webhook) and advanced tuning, all in the visual editor.

Example inputs

{ "query": "registered nurse", "location": "Dallas, TX", "maxResults": 100 }

{ "query": "data analyst", "location": "New York, NY", "radius": 25, "includeDetails": true }

{ "query": "warehouse", "location": "Texas", "incrementalMode": true, "stateKey": "tx-warehouse", "telegramToken": "...", "telegramChatId": "..." }

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💡 Use cases

• Recruiters & sourcers — pull fresh, structured openings with apply links and extracted contacts.
• Job boards & aggregators — ingest normalized listings with salary and geo-coordinates.
• Salary & market research — benchmark pay by title, region and category over time.
• Lead generation — find companies actively hiring, with contact details pulled from descriptions.
• Regional & mapping analysis — use lat/long and ZIP to map demand by area.
• AI agents & pipelines — compact, structured job data as a tool feed for LLM/MCP workflows.

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♻️ Incremental monitoring — pay for changes, not repeats

Schedule the actor and turn on incremental mode: each run compares against the last and emits only NEW / UPDATED / EXPIRED jobs — unchanged jobs are skipped before their detail page is even fetched, so a daily watch costs a fraction of a full re-scrape.

Daily churn	of 1,000 tracked	billable jobs	you save
5 %	1,000	50	95 %
15 %	1,000	150	85 %
30 %	1,000	300	70 %

The first run seeds the baseline and bills in full; every run after that bills only the delta.

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🚀 How to run it

1. Enter a search keyword (e.g. registered nurse) and a location (or leave it empty for nationwide) — or paste a Monster.com URL.
2. Set Max results and choose whether to fetch full details.
3. (Optional) Turn on incremental mode and a notification channel, then Schedule it.
4. Click Start.
5. Download the data as JSON, CSV or Excel, or pull it from the API.

New to Apify? Create a free account — it comes with monthly credit, no credit card required.

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🔌 Integrations & export

Export to JSON, CSV, Excel or an HTML table, or pull from the REST API and the JavaScript / Python clients. Runs on a schedule, connects to Google Sheets, Slack, Make, Zapier and n8n, and works as an MCP tool for AI agents — compact mode plus descriptionMaxLength keeps token usage small.

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❓ FAQ

How many jobs can I get? As many as the search returns — set maxResults to cap it (0 = unlimited).

Can I track a search over time? Yes — turn on incremental mode and schedule the actor; each run emits only what changed, so you never re-pay for unchanged jobs, and it can notify your channel.

Can I paste a URL from my browser? Yes — paste any Monster.com search URL into startUrls.

Which countries does it cover? Monster.com covers the United States.

Can I export to CSV / Excel / Google Sheets? Yes — from the dataset, or via the API, webhooks and Apify integrations (Make, Zapier, n8n).

Is it good for AI agents? Yes — enable compact mode; the output is MCP-friendly.

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⚖️ Disclaimer

This actor accesses only publicly available data on Monster.com. You are responsible for how you use the extracted data — in particular any personal information such as names, phone numbers or email addresses — and for complying with the site's terms and applicable law (including the GDPR where it applies). Not affiliated with, endorsed by, or sponsored by Monster.

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