Wellfound Startup Job Scraper — Equity Ranges & Job Alerts

What does Wellfound Startup Job Scraper do?

Wellfound Startup Job Scraper extracts structured job data from wellfound.com — including salary data, apply URLs, company metadata, full descriptions, remote-work indicators, location data, and skill tags. It supports keyword search, location filters, and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions and company metadata) where the source provides them.

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Key features

• 🚀 Startup equity & pay transparency: capture the equity range and salary band each startup lists inline, and filter by minimum equity and salary — focus on offers that match your compensation targets, data mainstream job boards don't expose.
• ♻️ Incremental mode: recurring runs emit and charge only for listings that are new or whose tracked content changed. First run builds the baseline; subsequent runs emit only NEW / UPDATED / REAPPEARED records (UNCHANGED + EXPIRED opt-in). Saves 80–95% on daily monitoring.
• 🔔 Notifications: Telegram, Slack, Discord, WhatsApp Cloud API, and generic webhook out of the box. Pair with incremental for daily new-listing alerts without pipeline glue.
• 📧 Email + phone extraction: best-effort regex extraction of contact emails and phone numbers from descriptions — emitted as extractedEmails[] and extractedPhones[] on every record.
• 💰 Structured salary: salary parsed into structured salaryMin / salaryMax / salaryCurrency / period — no string parsing on your side. Includes salaryHidden flag when the source filtered against a bracket but the listing itself doesn't disclose.
• 🎯 Batch searches: pass ["term1", "term2"] for query, location, or city to batch multiple searches in one run — shared dedup state, single dataset, one Actor-Start charge instead of N.
• 📦 Compact mode: AI-agent and MCP-friendly payloads with core fields only.
• ✂️ Description truncation: cap description length with descriptionMaxLength to control LLM prompt cost and dataset size — set 0 for full descriptions, or any char-limit to trim.
• 📌 Change classification: each record carries a changeType of NEW / UPDATED / UNCHANGED / REAPPEARED / EXPIRED. Default emits NEW + UPDATED + REAPPEARED; opt into the others with emitUnchanged / emitExpired.
• 📤 Export anywhere: Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
• 🔌 MCP connectors: export your results into Notion via Apify's MCP connectors — a clean run-summary page, no glue code. Opt-in via the App connector field; deterministic field-mapping, no AI. Built on Apify's connector framework, so more destinations open up as their catalog grows.

What data can you extract from wellfound.com?

Each result includes Core job fields (id, title, slug, portalUrl, compensation, salaryMin, salaryMax, and salaryEquityMin, and more), detail fields when enrichment is enabled (detailUrl, description, descriptionHtml, descriptionMarkdown, and primaryRoleTitle), apply information (directApply), and company metadata (companyId, companySlug, companyName, and companyLogo). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions and company metadata where the source provides them.

Input

The main inputs are a search keyword, an optional location filter, and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — Free-text job search (e.g. "rust engineer", "product designer"). A phrase Wellfound has a job category for searches that category; any other phrase keeps only jobs whose title contains its words. Combine with roles/searchUrls to union multiple searches.
• roles — Wellfound's built-in role categories. Each spawns a separate search thread. Common slugs: software-engineer, product-manager, product-designer, data-analyst, growth-marketer, sales-manager, account-manager, hr-manager, operations-manager, financial-analyst, graphic-designer, ui-ux-designer. Free-text — you can also try other slugs.
• searchUrls — Paste full Wellfound search URLs (e.g. https://wellfound.com/role/l/product-manager/berlin). Each URL spawns a separate search thread. Mix with query/roles freely — all deduplicate by job ID.
• location — One or more location slugs (e.g. ["new-york", "san-francisco", "berlin"]). Each location × role combination spawns a separate search thread. Ignored for pasted searchUrls (they carry their own location).
• excludeLocations — Drop jobs whose locationNames match any of these (substring match, e.g. "India" drops "Bangalore, India"). Applied after fetch.
• remote — When true, scrapes remote-filtered URLs. Ignored for pasted searchUrls. (default: false)
• originLocation — City name for radius-based filtering (e.g. "San Francisco", "Bengaluru", "London"), or coordinates as "latitude, longitude" (e.g. "40.7128, -74.0060"). Used with radiusMiles or radiusKm. Covers ~110 startup hubs worldwide; a city outside that list skips the radius filter with a warning, so use coordinates for it.
• radiusMiles — Keep only jobs within N miles of originLocation. Each job's own page supplies its coordinates and is fetched automatically when a radius is set, so radius runs take longer; a job whose page gives no coordinates is left out. US-standard unit. (default: 0)
• radiusKm — Kilometer alternative to radiusMiles. If both set, radiusMiles wins. Use this for EU-based searches. (default: 0)
• jobType — Filter by employment type. Matches Wellfound's jobType field (substring, case-insensitive).
• experienceLevel — Filter by minimum required years of experience. Entry: 0-2, Mid: 2-5, Senior: 5-8, Staff+: 8+. Jobs without experience data are kept.
• salaryMin — Drop jobs whose salary max is below this. 0 = no minimum. Jobs without salary data are dropped when minimum is set. (default: 0)
• ...and 45 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "query": "software engineer",
  "maxResults": 50
}

Filtered search — Narrow results with advanced filters — only matching jobs are returned.

→ Same field set as basic search; fewer, more relevant rows.

{
  "query": "software engineer",
  "jobType": "fulltime",
  "experienceLevel": "entry",
  "maxResults": 100
}

Incremental tracking — Only emit jobs that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "query": "software engineer",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "software-engineer-tracker"
}

Compact filtered output — Combine filters with compact mode for a lightweight AI-agent or MCP data source.

→ Core fields only — ideal for piping into LLMs or downstream tools without token overhead.

{
  "query": "software engineer",
  "jobType": "fulltime",
  "experienceLevel": "entry",
  "maxResults": 50,
  "compact": true
}

Output

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example job record

{
  "id": "4716549",
  "title": "Senior Software Engineer",
  "slug": "senior-software-engineer",
  "portalUrl": "https://wellfound.com/jobs/4716549-senior-software-engineer",
  "detailUrl": "https://wellfound.com/jobs/4716549-senior-software-engineer",
  "compensation": "$175k – $225k • 0.5% – 1.0%",
  "salaryMin": 175000,
  "salaryMax": 225000,
  "salaryEquityMin": "0.5%",
  "salaryEquityMax": "1.0%",
  "hasEquity": true,
  "description": "## Senior Software Engineer, Fintech Platform\n\n**Location:** USA\n\n**Experience:** 5+ years preferred\n\n### The role\n\nHourglass is building a business-finance platform through which commercial customers...",
  "descriptionHtml": "<h3>Senior Software Engineer, Fintech Platform</h3>\n<p><strong>Location:</strong> USA</p>\n\n<p><strong>Experience:</strong> 5+ years preferred</p>\n<h3>The role</h3>\n<p>Hourglass is building a business-...",
  "descriptionMarkdown": "### Senior Software Engineer, Fintech Platform\n\n**Location:** USA\n\n**Experience:** 5+ years preferred\n\n### The role\n\nHourglass is building a business-finance platform through which commercial customer...",
  "jobType": "full-time",
  "postedAt": "2026-09-14T18:03:48Z",
  "locationNames": [
    "New York City",
    "San Francisco Bay Area"
  ],
  "remote": false,
  "remoteKind": "ONSITE",
  "wfhFlexible": true,
  "yearsExperienceMin": 5,
  "autoPosted": false,
  "primaryRoleTitle": "Software Engineer",
  "skills": [
    "TypeScript"
  ],
  "companyId": "9591839",
  "companySlug": "hourglass-financial",
  "companyName": "Hourglass",
  "companyLogo": "https://photos.wellfound.com/startups/i/9591839-f15e93166aa81b7283a976cbd774d6be-medium_jpg.jpg?buster=1789415876",
  "companyTagline": "Business moves fast. Money should too",
  "companySize": "SIZE_1_10",
  "companySizeLabel": "1-10",
  "companyBadges": [
    "Actively Hiring",
    "B2B",
    "Early Stage"
  ],
  "companyStage": "early_stage",
  "companyBusinessType": "B2B",
  "companyActivelyHiring": true,
  "companyGrowingFast": false,
  "companyTopInvestors": false,
  "companyYcFunded": false,
  "companyFundingStage": "Early Stage",
  "salaryCurrency": "USD",
  "salaryPeriod": "YEAR",
  "companyWebsite": "https://hourglass.com",
  "industry": "Finance Technology",
  "directApply": true,
  "datePosted": "2026-09-14T18:03:48Z",
  "jobAddressLocality": "New York",
  "jobAddressRegion": "New York",
  "jobAddressCountry": "United States",
  "latitude": 40.7128,
  "longitude": -74.0059,
  "isRepost": false,
  "matchedSearch": "role=software-engineer@new-york",
  "source": "wellfound",
  "scrapedAt": "2026-09-15T21:29:42.417Z"
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED. Default output covers NEW / UPDATED / REAPPEARED; set emitUnchanged: true or emitExpired: true to opt into the others.
• firstSeenAt, lastSeenAt — ISO-8601 timestamps tracking the listing across runs.
• isRepost, repostOfId, repostDetectedAt — populated when a new listing matches the tracked content of a previously expired one. Set skipReposts: true to drop detected reposts from the output.

How to scrape wellfound.com

1. Go to Wellfound Startup Job Scraper in Apify Console.
2. Enter a search keyword and optional location filter.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions, company data.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract job data from wellfound.com for market research and competitive analysis.
• Track salary trends across regions and categories over time.
• Monitor new and changed jobs on scheduled runs without processing the full dataset every time.
• Auto-apply or feed apply URLs into your ATS / hiring pipeline.
• Research company hiring patterns, employer profiles, and industry distribution.
• Use structured location data for regional analysis, mapping, and geo-targeting.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.
• Analyze skill demand across listings using structured skill tags.
• Collect ratings and reviews for reputation monitoring and benchmarking.

How much does it cost to scrape wellfound.com?

Wellfound Startup Job Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.003 per job record

Example costs:

• 10 results: $0.035
• 25 results: $0.08
• 100 results: $0.3
• 200 results: $0.6
• 500 results: $1.5

Platform usage is billed on top of these prices. The figures above are Apify event fees only. Compute units and proxy traffic are billed separately by Apify at your actual consumption, and for proxy- or browser-heavy workloads they can exceed the event fees. What you actually pay depends on your own input — how many results you request, whether detail enrichment is on, the run memory you choose, and your proxy settings — so it cannot be stated as a fixed figure here. Every run reports its exact platform usage on the run detail page.

The Actor Start event is charged once per GB of run memory (minimum one), so a run configured with 2 GB is charged two start events rather than one.

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of jobs that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 jobs per run, daily polling (30 runs/month). Costs scale linearly with the number of jobs.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.76	$0.04	$0.71 (94%)	$1.27
15% — moderate broad query	$0.76	$0.12	$0.64 (84%)	$3.53
30% — high-volume aggregator	$0.76	$0.23	$0.53 (70%)	$6.90

Full re-scrape monthly cost at the same cadence: $22.65. First month with incremental costs $1.99 / $4.16 / $7.42 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage (see the note above) also applies to these figures. Incremental runs consume less of it on result processing, though the fixed per-run overhead stays the same.

FAQ

How many results can I get from wellfound.com?

The number of results depends on the search query and available jobs on wellfound.com. Use the maxResults parameter to control how many results are returned per run.

Does Wellfound Startup Job Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed jobs on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Wellfound Startup Job Scraper with other apps?

Yes. Wellfound Startup Job Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Wellfound Startup Job Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Wellfound Startup Job Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, descriptionMaxLength, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape wellfound.com?

This actor extracts publicly available data from wellfound.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

How do I search by keyword or tech stack?

Use query for free text (e.g. "rust engineer", "staff engineer", "product designer"). A phrase Wellfound has a job category for searches that category; any other phrase keeps only jobs whose title contains its words, and the run log says which of the two happened. Add roles for Wellfound's own categories (software-engineer, product-manager, data-scientist, and others) and paste Wellfound search URLs into searchUrls. All three combine into one deduplicated run. To narrow further, includeKeywords filters on title, description and skills, and includeCompanies / excludeCompanies keep or drop specific startups.

How fresh are the job listings?

Very fresh. Sampling 48 jobs from the default engineer search in April 2026 yielded:

Age	Share
1-24 hours	43%
1-7 days	45%
7-30 days	10%
> 30 days	0%

Median age was 1.5 days; the newest listing was 1.25 hours old at scrape time; the oldest was 28 days. Wellfound appears to expire inactive postings aggressively, so you do not see months-old zombie listings that plague broader job aggregators. This makes hourly scheduled runs with incrementalMode and maxAgeMinutes: 60 genuinely useful for real-time monitoring — roughly 1-5% of the feed turns over each hour.

Why are some enrichment fields null?

Every listing already carries the full description, compensation, equity, experience, job type, and the company's name, logo, size, stage and badges (for example Actively Hiring, YC Funded, Top Investors, response time and employee ratings). Salary currency and period, company website, industry, office address, coordinates, skills, benefits and apply type come from each job's own page, which the actor opens when enrichDetail is on and whenever a radius is set. A field stays null when the job's page does not state it.

Deep company-profile fields (founders, funding rounds and amount raised, social links, founded year) are not available from Wellfound's public pages and stay null, with or without enrichCompany.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.

Disclaimer

This actor accesses only publicly available data on wellfound.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 Wellfound Startup's terms of use, applicable data-protection law (including the GDPR where it applies), and the anti-spam rules of your jurisdiction.

This actor is not affiliated with, endorsed by, or connected to Wellfound Startup.

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