Wellfound (AngelList) Jobs & Startup Scraper

Scrape Wellfound (formerly AngelList Talent) for startup job listings, company profiles, team members, funding data, and hiring information. Built for recruiters, venture capitalists, startup analysts, and anyone researching the startup ecosystem.

Features

• Startup Job Listings -- titles, salaries, equity ranges, skills, remote status, visa sponsorship
• Company Profiles -- name, industry, size, funding stage, total raised, investors, team members
• Team & Founder Data -- names, roles, LinkedIn/Twitter links, avatars
• Funding Intelligence -- rounds, amounts, investors, stage progression
• Multi-Strategy Parsing -- __NEXT_DATA__ extraction, GraphQL cache, JSON-LD, DOM fallback
• Search & Direct Scraping -- search by keyword/location/role or scrape specific company URLs
• Automatic Pagination -- cursor-based and offset-based page traversal
• User-Agent Rotation -- 10 rotating modern browser signatures
• Rate Limiting -- built-in delays and respectful crawling practices
• PPE Billing -- pay only for results successfully scraped ($0.006 per result)
• Proxy Support -- works with Apify residential proxies for reliable large-scale runs

Input Parameters

Parameter	Type	Default	Description
searchTerms	array	["software engineer"]	Keywords to search for. Each triggers a separate search.
location	string	""	Filter by location (e.g., "San Francisco", "Remote", "Berlin")
role	enum	"any"	any, full-time, part-time, contract, internship, cofounder
companyUrls	array	[]	Direct Wellfound company URLs to scrape
maxResults	integer	300	Maximum total results to scrape (0 = unlimited)
proxy	object	-	Apify proxy configuration (recommended for large runs)

Example Input -- Job Search

{
    "searchTerms": ["machine learning engineer", "data scientist"],
    "location": "San Francisco",
    "role": "full-time",
    "maxResults": 100,
    "proxy": {
        "useApifyProxy": true,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
}

Example Input -- Company Profiles

{
    "searchTerms": [],
    "companyUrls": [
        "https://wellfound.com/company/stripe",
        "https://wellfound.com/company/notion",
        "https://wellfound.com/company/figma"
    ],
    "maxResults": 50
}

Example Input -- Combined Search + Companies

{
    "searchTerms": ["react developer"],
    "location": "Remote",
    "role": "full-time",
    "companyUrls": [
        "https://wellfound.com/company/vercel"
    ],
    "maxResults": 200,
    "proxy": {
        "useApifyProxy": true
    }
}

Output -- Job Listings

Each scraped job contains the following fields:

Field	Type	Description
type	string	Always "job" for job listings
title	string	Job title (e.g., "Senior Backend Engineer")
company	string	Company name
salary	string	Salary range (e.g., "$150,000 - $200,000")
salaryParsed	object	Structured salary with min, max, currency
equity	string	Equity range (e.g., "0.5% - 1.0%")
equityParsed	object	Structured equity with minPercent, maxPercent
location	string	Job location
remote	boolean	Whether the position is remote
skills	array	Required skills/technologies (e.g., ["Python", "AWS", "Kubernetes"])
description	string	Full job description
roleType	string	Employment type (full-time, contract, etc.)
experienceLevel	string	Required experience level
visaSponsorship	boolean	Whether visa sponsorship is offered
postedAt	string	Posting date (ISO 8601)
jobId	string	Wellfound job ID
jobUrl	string	Direct link to job posting
companySlug	string	Company URL slug
searchTerm	string	The search term that found this job
scrapedAt	string	Timestamp when scraped

Example Job Output

{
    "type": "job",
    "title": "Senior Full-Stack Engineer",
    "company": "Notion",
    "salary": "$170,000 - $250,000",
    "salaryParsed": {
        "raw": "$170,000 - $250,000",
        "min": 170000,
        "max": 250000,
        "currency": "USD"
    },
    "equity": "0.01% - 0.05%",
    "equityParsed": {
        "raw": "0.01% - 0.05%",
        "minPercent": 0.01,
        "maxPercent": 0.05
    },
    "location": "San Francisco, CA",
    "remote": true,
    "skills": ["React", "TypeScript", "Node.js", "PostgreSQL", "AWS"],
    "description": "We are looking for a Senior Full-Stack Engineer to help build the future of productivity tools...",
    "roleType": "Full-Time",
    "experienceLevel": "Senior",
    "visaSponsorship": true,
    "postedAt": "2026-02-25T00:00:00.000Z",
    "jobId": "2847291",
    "jobUrl": "https://wellfound.com/company/notion/jobs/2847291-senior-full-stack-engineer",
    "companySlug": "notion",
    "searchTerm": "full-stack engineer",
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

Output -- Company Profiles

Each scraped company contains the following fields:

Field	Type	Description
type	string	Always "company" for company profiles
name	string	Company name
slug	string	Wellfound URL slug
tagline	string	One-line company description
description	string	Full company description
industry	string	Industry/market
size	string	Employee count range
stage	string	Funding stage (Seed, Series A, etc.)
funding	string	Total funding raised
fundingRounds	array	Individual funding rounds with amounts and dates
location	string	Company headquarters
website	string	Company website URL
logo	string	Company logo URL
founded	string	Year founded
team	array	Team members with name, role, avatar, linkedin, twitter
investors	array	Investors with name and type
social	object	Social media links (twitter, linkedin, github, etc.)
openJobs	number	Number of open positions
companyUrl	string	Wellfound profile URL
scrapedAt	string	Timestamp when scraped

Example Company Output

{
    "type": "company",
    "name": "Stripe",
    "slug": "stripe",
    "tagline": "Financial infrastructure for the internet",
    "description": "Stripe is a technology company that builds economic infrastructure for the internet...",
    "industry": "Financial Technology, Payments",
    "size": "5001+",
    "stage": "Series I",
    "funding": "$8.7B",
    "fundingRounds": [
        {
            "type": "Series I",
            "amount": "$6.5B",
            "date": "2023-03-15"
        }
    ],
    "location": "San Francisco, CA",
    "website": "https://stripe.com",
    "logo": "https://photos.wellfound.com/startups/i/...",
    "founded": "2010",
    "team": [
        {
            "name": "Patrick Collison",
            "role": "CEO & Co-Founder",
            "avatar": "https://...",
            "linkedin": "https://linkedin.com/in/patrickcollison",
            "twitter": "https://twitter.com/patrickc"
        },
        {
            "name": "John Collison",
            "role": "President & Co-Founder",
            "avatar": "https://...",
            "linkedin": "https://linkedin.com/in/johncollison",
            "twitter": "https://twitter.com/collision"
        }
    ],
    "investors": [
        { "name": "Sequoia Capital", "type": "Venture Capital" },
        { "name": "Andreessen Horowitz", "type": "Venture Capital" }
    ],
    "social": {
        "twitter": "https://twitter.com/stripe",
        "linkedin": "https://linkedin.com/company/stripe",
        "github": "https://github.com/stripe"
    },
    "openJobs": 47,
    "companyUrl": "https://wellfound.com/company/stripe",
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

Use Cases

Recruiters & Talent Acquisition

• Find startup jobs matching specific skills and technologies
• Identify companies actively hiring for niche roles
• Track salary and equity benchmarks across the startup ecosystem
• Build candidate sourcing lists from team directories

Venture Capital & Investors

• Research startups by funding stage, industry, and growth signals
• Track founding team backgrounds and investor networks
• Monitor hiring velocity as a proxy for company growth
• Build deal flow pipelines with structured company data

Startup Ecosystem Research

• Analyze which technologies and skills are most in demand
• Track remote work adoption across startup stages
• Study salary and equity trends by role, location, and company size
• Map investor-startup networks and funding patterns

Job Seekers & Career Planning

• Find startup jobs filtered by your exact skill set
• Compare compensation (salary + equity) across companies
• Research company culture, team, and funding before applying
• Track which startups are scaling fastest (hiring velocity)

Market Intelligence

• Monitor competitor hiring to understand their strategic priorities
• Track industry trends through job posting analysis
• Identify emerging technology stacks from skill requirements
• Benchmark your company's compensation against the market

Tips for Best Results

1. Start Small -- test with maxResults: 20 before scaling up to verify output quality.
2. Use Proxies for Scale -- Wellfound may rate-limit datacenter IPs. Use Apify residential proxies for runs exceeding 100 results.
3. Specific Search Terms -- more specific terms yield better results (e.g., "senior react engineer" rather than "developer").
4. Combine Strategies -- use searchTerms for broad discovery and companyUrls for targeted deep scraping.
5. Location Filters -- use city names as they appear on Wellfound (e.g., "San Francisco" not "SF").
6. Monitor Costs -- at $0.006 per result, 1000 results costs $6. Set maxResults to control spend.

Limitations

• Wellfound may change their page structure at any time. The scraper uses 4 fallback parsing strategies to handle this.
• Salary and equity data is only available when the company chooses to display it (approximately 60-70% of listings).
• Some company profile details (investors, funding rounds) may require a logged-in view -- the scraper extracts what is publicly available.
• Rate limiting may occur on large runs without proxy configuration.
• Historical job listings (closed/filled positions) are generally not available.

Cost / Pricing

This actor uses Pay-Per-Event (PPE) pricing. You are charged $0.006 per result successfully scraped (either a job listing or a company profile). There is no charge for failed requests, blocked pages, or duplicate results that are filtered out.

Results	Cost
100	$0.60
500	$3.00
1,000	$6.00
5,000	$30.00

Technical Details

• Built with CheerioCrawler from Crawlee (no browser overhead -- fast and efficient)
• ESM modules (Node.js 18+)
• 4-layer parsing strategy: __NEXT_DATA__ > GraphQL cache > JSON-LD > DOM selectors
• Handles Cloudflare challenges, CAPTCHAs, and rate limiting with retry logic
• Automatic deduplication by job URL and company slug
• Structured salary and equity parsing into machine-readable formats
• Cursor-based and offset-based pagination support

Integration — Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("sovereigntaylor/wellfound-scraper").call(run_input={
    "searchTerm": "wellfound",
    "maxResults": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item.get('title', item.get('name', 'N/A'))}")

Integration — JavaScript

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('sovereigntaylor/wellfound-scraper').call({
    searchTerm: 'wellfound',
    maxResults: 50
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(item.title || item.name || 'N/A'));