How to use Wellfound Jobs Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$2.70 per 1,000 jobs (live Apify price on 2026-11-03).

1. In the Apify Store (no code)

1. Open arcadium/wellfound-jobs and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "roles": [
    "software engineer"
  ],
  "locations": [
    "New York"
  ],
  "startUrls": [],
  "maxJobsPerSearch": 50
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~wellfound-jobs/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"roles":["software engineer"],"locations":["New York"],"startUrls":[],"maxJobsPerSearch":50}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/wellfound-jobs').call({
  "roles": [
    "software engineer"
  ],
  "locations": [
    "New York"
  ],
  "startUrls": [],
  "maxJobsPerSearch": 50
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/wellfound-jobs").call(run_input={
    "roles": ["software engineer"],
    "locations": ["New York"],
    "startUrls": [],
    "maxJobsPerSearch": 50,
})
items = client.dataset(run.default_dataset_id).list_items().items  # apify-client 3.x returns a Run object
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/wellfound-jobs",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~wellfound-jobs/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"roles\":[\"software engineer\"],\"locations\":[\"New York\"],\"startUrls\":[],\"maxJobsPerSearch\":50}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/wellfound-jobs",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/wellfound-jobs")
rows = tool.invoke({"run_input": {
    "roles": ["software engineer"],
    "locations": ["New York"],
    "startUrls": [],
    "maxJobsPerSearch": 50,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "job",
  "platform": "wellfound",
  "jobId": "4844846",
  "url": "https://wellfound.com/jobs/4844846-software-engineer",
  "title": "Software Engineer",
  "role": "Software Engineer",
  "jobType": "full-time",
  "remote": false,
  "remoteKind": null,
  "wfhFlexible": null,
  "locations": [
    "New York"
  ],
  "acceptedRemoteLocations": [],
  "compensation": "$142k – $187k",
  "salary": {
    "min": 142000,
    "max": 187000,
    "currency": "USD",
    "period": "year",
    "source": "structured"
  },
  "equityMinPercent": null,
  "equityMaxPercent": null,
  "yearsExperienceMin": null,
  "yearsExperienceMax": null,
  "postedAt": "2026-10-15T00:02:06Z",
  "description": "## About Check\n\nPayroll is broken. It's still running on decades-old infrastructure. **We're rebuilding it.** ...",
  "atsSource": "ashby",
  "autoPosted": false,
  "companyId": "8170097",
  "companyName": "Check",
  "companySlug": "checkhq",
  "companyUrl": "https://wellfound.com/company/checkhq",
  "companyTagline": "Check’s mission is to make paying people simple",
  "companySize": "51-200",
  "companyStage": "Growth Stage",
  "companyBadges": [
    "Actively Hiring",
    "B2B"
  ],
  "ycFunded": false,
  "topInvestors": true,
  "recentlyFunded": false,
  "valuation": "Valuation $500M+",
  "companyLogoUrl": "https://photos.wellfound.com/startups/i/8170097-670b14dbd6d668e33d45382818a7008d-medium_jpg.jpg",
  "companyWebsite": "https://checkhq.com/",
  "industries": [
    "SaaS",
    "Enterprise Software"
  ],
  "jobLocations": [
    {
      "locality": null,
      "region": "New York",
      "country": "United States"
    }
  ],
  "directApply": true,
  "validThrough": null,
  "detailsIncluded": true,
  "source": "software engineer in new york",
  "sourceType": "roleLocation",
  "searchRole": "software-engineer",
  "searchLocation": "new-york",
  "position": 1,
  "scrapedAt": "2026-10-22T12:00:00.000Z"
}

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.