# Wellfound Jobs Scraper (`benthepythondev/wellfound-jobs-scraper`) Actor - **URL**: https://apify.com/benthepythondev/wellfound-jobs-scraper.md - **Developed by:** [Ben](https://apify.com/benthepythondev) (community) - **Categories:** Jobs, Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Wellfound Jobs Scraper - Startup Jobs Collect public startup job listings from **Wellfound**, formerly AngelList Talent, and export them as structured recruitment and market-intelligence data. Search one or more locations, match role or skill keywords, filter remote jobs and compensation, and receive full public descriptions plus startup metadata in every result. The Actor returns job titles, descriptions, salary and equity ranges, locations, remote policy, experience requirements, startup name, size, profile links, logos, hiring badges and publication times. Results can be downloaded as JSON, CSV, Excel or XML, scheduled in Apify, or consumed through the dataset API. ### What can you use this Actor for? - Monitor startup and technology hiring by city, country or region. - Find roles that disclose salary or equity before applying. - Build startup hiring-signal lists for sales, recruiting or investment research. - Compare remote-work availability across startup markets. - Track companies growing in engineering, data, product, sales or operations. - Create a job feed, alerting workflow, spreadsheet or labor-market dashboard. - Analyze compensation bands, experience requirements and startup size. - Discover recently published roles without manually checking location pages. ### Why scrape Wellfound jobs? Wellfound focuses on startups and technology companies. Its public job records often contain data that general job boards omit: salary bands, equity ranges, accepted remote locations, startup size, role category and company positioning. That makes the source useful for candidates, recruiters, analysts, founders and B2B teams. The Actor reads the structured Apollo state that Wellfound publishes on its public location pages. A single page can contain dozens of job records with complete descriptions and the associated startup entities. This direct HTTP design avoids a full browser and does not require a Wellfound account, paid proxy or third-party API key. ### Input The default run searches Berlin for `software engineer` and returns up to 10 jobs. Use country-scale pages such as `United States` for broader research, or city pages such as `Berlin`, `London` and `New York` for a focused dataset. | Field | Purpose | |---|---| | `locations` | Wellfound location pages to collect, expressed as readable city, country or region names. | | `keywords` | Terms matched against title, description, role, startup name and company summary. | | `remoteOnly` | Keep only jobs marked remote or remote-capable by Wellfound. | | `jobTypes` | Optional full-time, part-time, contract or internship filters. | | `minimumSalary` | Minimum lower bound of the published salary range. | | `includeJobsWithoutSalary` | Decide whether jobs without disclosed compensation remain in output. | | `maxResultsPerLocation` | Maximum unique matching jobs saved for each location. | | `maxPagesPerLocation` | Safety cap for Wellfound pagination. | #### Example input ```json { "locations": ["Berlin", "United States"], "keywords": ["python", "machine learning"], "remoteOnly": true, "jobTypes": ["full-time"], "minimumSalary": 100000, "includeJobsWithoutSalary": false, "maxResultsPerLocation": 25, "maxPagesPerLocation": 10 } ```` Keywords use an OR match: a row is included when any requested term appears in its title, full public description, primary role, startup name or company summary. Jobs that appear in more than one location are deduplicated by Wellfound job ID. ### Output Each dataset item is one public startup job. Common fields include: - `job_id`, `url`, `title`, `description` - `job_type`, `primary_role` - `locations`, `accepted_remote_locations` - `is_remote`, `remote_kind`, `work_from_home_flexible` - `years_experience_minimum`, `years_experience_maximum` - `compensation`, `salary_minimum`, `salary_maximum`, `salary_currency` - `equity_minimum_percent`, `equity_maximum_percent` - `published_at`, `is_auto_posted`, `ats_source` - `company_id`, `company_name`, `company_url`, `company_logo` - `company_size`, `company_high_concept`, `company_badges` - `lead_score`, `source_location`, `search_keywords` - `search_rank`, `source_url`, `scraped_at` #### Example result ```json { "job_id": "4420215", "url": "https://wellfound.com/jobs/4420215-middle-data-engineer", "title": "Middle Data Engineer", "job_type": "full-time", "primary_role": "Data Engineer", "locations": ["Berlin", "Rome", "Madrid"], "accepted_remote_locations": ["Europe"], "is_remote": true, "compensation": "$35k – $48k • No equity", "salary_minimum": 35000, "salary_maximum": 48000, "salary_currency": "USD", "years_experience_minimum": 3, "company_name": "UVIK Software", "company_size": "SIZE_11_50" } ``` ### Compensation and equity The Actor preserves Wellfound's original `compensation` text and also parses common dollar, euro and pound ranges into numeric fields. Values with `k` or `m` suffixes are normalized to complete numbers. Equity percentages are returned separately when the employer publishes them. The `salary_currency` field is essential when combining several markets. Do not compare numeric salary values across currencies without conversion. Missing compensation stays empty, and the Actor never invents or estimates a salary. ### Startup hiring intelligence Every job is joined to the startup entity published on the same page. The output can include the startup name, public profile, logo, team-size band, one-line positioning and badges such as actively hiring, recently funded, fast growing or top responder. `lead_score` is a transparent completeness score, not a prediction. It rewards rows with a named startup, full description, compensation, equity, logo, company positioning, locations and role metadata. Use it to prioritize records for manual review, not to automate consequential decisions. ### Reliable extraction design The Actor uses browser-compatible HTTP fingerprints to fetch public location pages, then reads their Next.js Apollo state. It does not depend on fragile visual selectors. Pagination is bounded, overlapping jobs are deduplicated by source ID, and temporary failures are retried with short backoff. Runs fail visibly when no requested job matches are found. This avoids presenting an empty dataset as a healthy extraction. Defaults are intentionally small enough for a quick first run and Apify's automated quality tests, while production limits remain configurable. ### Scheduling and integrations Run the Actor daily to monitor a stable list of locations and keywords. Use `job_id` as the external primary key and alert only when a new value appears. A recruiting team can route new matches to Slack or email; an analyst can append snapshots to a warehouse; a founder can monitor competing startups' hiring activity. Apify datasets can be exported directly or connected to Make, Zapier, n8n, webhooks and cloud storage. Use CSV or Excel for ad hoc research and JSON for recurring applications. ### Pricing This Actor uses transparent pay-per-event pricing. A tiny start event covers initialization, and each unique job pushed to the default dataset is billed as one `result`. Records rejected by filters or removed as duplicates are not billed as dataset results. The result price is positioned below the current high-usage Wellfound competitors while retaining full descriptions, compensation, equity and startup context. ### Responsible use The Actor collects public job and company information. Use it for legitimate job search, recruitment, research, monitoring and integration workflows. Follow applicable privacy, employment, marketing and database rules. Verify current details on the canonical Wellfound page because jobs can change or expire after collection. ### FAQ #### Does this require a Wellfound login? No. The Actor reads public location-page data without a user account. #### Can I search by role? Yes. Add role or skill terms to `keywords`. They are matched against the title, primary role and full description as well as startup text. #### Can I collect remote startup jobs? Enable `remoteOnly`. Inspect `remote_kind` and `accepted_remote_locations` for the source's more detailed remote restrictions. #### Why are some salary or equity values empty? Employers choose which compensation fields to publish. Keep `includeJobsWithoutSalary` disabled when your workflow requires a disclosed salary. #### How do I search New York or the United States? Enter readable values such as `New York` or `United States`. The Actor converts them to Wellfound location slugs. #### Why did a very narrow search return no rows? The requested keywords and filters may not match the pages scanned. Increase `maxPagesPerLocation`, broaden the keywords, allow jobs without salary, or remove the remote-only filter. #### How do I deduplicate scheduled runs? Use `job_id` as the primary key and `url` as a fallback. The Actor already removes duplicates within each run. #### How can I report a source change? Open an issue from the Actor page and include a small input plus the failed run URL. Do not include login details or private candidate information. ### You might also like - [Welcome to the Jungle Jobs Scraper](https://apify.com/benthepythondev/welcome-to-the-jungle-jobs-scraper) for European startup and employer-profile jobs. - [Y Combinator Jobs Scraper](https://apify.com/benthepythondev/yc-jobs-scraper) for YC startup vacancies. - [Hiring Cafe Scraper](https://apify.com/benthepythondev/hiring-cafe-scraper) for broad technology job discovery. - [Remote Jobs Aggregator](https://apify.com/benthepythondev/remote-jobs-aggregator) for multi-source remote roles. If this Actor saves you time, please leave a review on its Apify Store page. Reviews help prioritize reliability fixes and requested filters. **Keywords:** Wellfound scraper, Wellfound jobs scraper, AngelList jobs API, startup jobs scraper, startup hiring data, remote startup jobs, tech jobs API, salary equity data, startup lead generation, recruitment intelligence, Wellfound API, Apify jobs scraper ### Production workflow This Actor is designed for job seekers, startup recruiters, labor-market analysts and sales teams monitoring hiring signals. Schedule focused location and role searches, deduplicate by job\_id, and compare each dataset with the previous run to identify new openings, compensation changes and companies accelerating hiring. Small, repeatable runs are easier to audit than occasional unlimited exports: they expose source changes quickly, reduce duplicate processing and keep automation costs predictable. Use the input defaults as a smoke test, then widen the query only after confirming that the returned fields match the downstream workflow. Treat each dataset row as source evidence captured at a point in time, not as a permanent fact. Preserve the canonical identifier and source URL, add the run timestamp in the receiving system, and deduplicate before writing to a CRM, spreadsheet, warehouse or alert channel. For recurring monitoring, compare the newest dataset with the previous successful run and send only additions or material changes onward. That pattern avoids repeated notifications and makes the automation more useful over time. ### Data quality and interpretation The primary output includes `job_id`, `url`, `title`, `description`, `job_type`, `primary_role`, `locations`, `accepted_remote_locations`, `is_remote`, `remote_kind`, `work_from_home_flexible`, `years_experience_minimum`. Availability depends on what the source publishes for a particular result. Empty values are retained as null or empty fields rather than invented. Search rank is source-specific and can change with language, geography, query wording and time. Validate critical decisions against the linked source page, especially when the data is used for outreach, compliance, pricing, routing or reporting. Use bounded inputs and respect the source's published interface. The Actor does not bypass logins or collect private account data. Avoid placing passwords, session cookies, personal access tokens or confidential records in Actor input. If a public source changes its response shape, attach a reproducible public input and run ID to an Actor issue so the parser can be updated without exposing private data. ### Automation and cost control The default dataset can be exported as JSON, CSV, Excel, XML or RSS and can be consumed through the Apify API. In Make, Zapier or n8n, start the Actor, wait for the run to finish, then read default-dataset items. In code, store the Actor run ID and dataset ID alongside the imported rows so every record remains traceable. Apply filters after collection only when the source cannot express them directly; source-side filtering usually reduces cost and noise. This Actor uses pay-per-event billing. A result event is charged only when a structured row is saved to the default dataset. Keep test runs small, estimate expected row volume before scheduling broad searches, and use platform spending limits for unattended workflows. Failed requests and empty searches should not create result events. ### Support and feedback If the Actor saves time in a real workflow, please leave a short Apify Store review describing the use case and the fields that were most useful. Reviews help other users evaluate the product and help prioritize maintenance. For support, open an Actor issue with the run ID, public input, expected behavior and one affected field. Do not include credentials or confidential information. # Actor input Schema ## `locations` (type: `array`): Cities, countries or regions represented by Wellfound location pages, for example Berlin, London or United States. ## `keywords` (type: `array`): Match roles, skills, startup names or text in the public job description. Leave empty to include all jobs in each location. ## `remoteOnly` (type: `boolean`): Return only listings Wellfound marks as remote or remote-capable. ## `jobTypes` (type: `array`): Optional Wellfound job-type filters. ## `minimumSalary` (type: `integer`): Minimum lower bound of the published salary range. Currency is reported separately per job. ## `includeJobsWithoutSalary` (type: `boolean`): Keep listings where the employer did not publish a compensation range. ## `maxResultsPerLocation` (type: `integer`): Maximum unique matching jobs saved for each Wellfound location. ## `maxPagesPerLocation` (type: `integer`): Safety cap for Wellfound location-page pagination. ## Actor input object example ```json { "locations": [ "Berlin", "United States" ], "keywords": [ "software engineer", "python" ], "remoteOnly": false, "jobTypes": [], "minimumSalary": 0, "includeJobsWithoutSalary": true, "maxResultsPerLocation": 10, "maxPagesPerLocation": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "locations": [ "Berlin", "United States" ], "keywords": [ "software engineer", "python" ], "remoteOnly": false, "jobTypes": [], "minimumSalary": 0, "includeJobsWithoutSalary": true, "maxResultsPerLocation": 10, "maxPagesPerLocation": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("benthepythondev/wellfound-jobs-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "locations": [ "Berlin", "United States", ], "keywords": [ "software engineer", "python", ], "remoteOnly": False, "jobTypes": [], "minimumSalary": 0, "includeJobsWithoutSalary": True, "maxResultsPerLocation": 10, "maxPagesPerLocation": 10, } # Run the Actor and wait for it to finish run = client.actor("benthepythondev/wellfound-jobs-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "locations": [ "Berlin", "United States" ], "keywords": [ "software engineer", "python" ], "remoteOnly": false, "jobTypes": [], "minimumSalary": 0, "includeJobsWithoutSalary": true, "maxResultsPerLocation": 10, "maxPagesPerLocation": 10 }' | apify call benthepythondev/wellfound-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benthepythondev/wellfound-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wellfound Jobs Scraper", "version": "1.0", "x-build-id": "GbnfczgZLLRyr7CcE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benthepythondev~wellfound-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benthepythondev-wellfound-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/benthepythondev~wellfound-jobs-scraper/runs": { "post": { "operationId": "runs-sync-benthepythondev-wellfound-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/benthepythondev~wellfound-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-benthepythondev-wellfound-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "locations" ], "properties": { "locations": { "title": "Wellfound locations", "type": "array", "description": "Cities, countries or regions represented by Wellfound location pages, for example Berlin, London or United States.", "default": [ "Berlin" ], "items": { "type": "string" } }, "keywords": { "title": "Job keywords", "type": "array", "description": "Match roles, skills, startup names or text in the public job description. Leave empty to include all jobs in each location.", "default": [ "software engineer" ], "items": { "type": "string" } }, "remoteOnly": { "title": "Remote-capable jobs only", "type": "boolean", "description": "Return only listings Wellfound marks as remote or remote-capable.", "default": false }, "jobTypes": { "title": "Job types", "type": "array", "description": "Optional Wellfound job-type filters.", "items": { "type": "string", "enum": [ "full-time", "part-time", "contract", "internship" ] }, "default": [] }, "minimumSalary": { "title": "Minimum published salary", "minimum": 0, "type": "integer", "description": "Minimum lower bound of the published salary range. Currency is reported separately per job.", "default": 0 }, "includeJobsWithoutSalary": { "title": "Include jobs without salary", "type": "boolean", "description": "Keep listings where the employer did not publish a compensation range.", "default": true }, "maxResultsPerLocation": { "title": "Maximum jobs per location", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum unique matching jobs saved for each Wellfound location.", "default": 10 }, "maxPagesPerLocation": { "title": "Maximum pages per location", "minimum": 1, "maximum": 50, "type": "integer", "description": "Safety cap for Wellfound location-page pagination.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```