WeWorkRemotely Jobs Scraper

What does WeWorkRemotely Jobs Scraper do?

WeWorkRemotely Jobs Scraper is a WeWorkRemotely scraper and remote job data extractor for collecting public job listings in a structured dataset. Choose all supported job categories or one category, optionally add WeWorkRemotely search URLs for keyword filtering, and set the maximum number of results to return.

Each result can include the job title, company, category, categories, remote location, region, country, state, salary text, parsed salary values, currency, salary interval, employment type, skills, posting date, expiry date, descriptions, company website, and application URL. The dataset is useful for remote hiring research, salary analysis, talent sourcing, job alerts, and recurring market monitoring.

Why use WeWorkRemotely Jobs Scraper?

• Remote hiring intelligence - Build a current view of roles being advertised across programming, design, product, support, sales, marketing, finance, and other categories.
• Focused job discovery - Use search URLs containing terms such as python, react, DevOps, customer success, or product manager to narrow the collected listings.
• Structured compensation data - Keep the original salary text and receive parsed minimum, maximum, currency, and pay-period fields when the listing provides enough information.
• Richer job records - Collect company, location, skills, job type, dates, descriptions, source URL, company website, and application URL in one dataset.
• Cleaner datasets - Jobs appearing in more than one category are merged by listing URL, which reduces duplicate records in broad category runs.
• Automation-ready exports - Download results as JSON, CSV, Excel, XML, or use Apify datasets, schedules, webhooks, and integrations in a larger workflow.

What data can you extract from WeWorkRemotely?

The actor returns one dataset item per unique job listing. Fields are included when they are available in the public listing data.

Field	Type	Description
title	String	Job title.
company	String	Hiring company name when it is included in the listing title.
category	String	Primary WeWorkRemotely category.
categories	Array of String	All category labels associated with the listing.
location	String	Normalized location or Remote when the description clearly identifies the role as remote.
region	String	Published region for the job, when available.
country	String	Published country, when available.
state	String	Published state or province, when available.
salary	String	Original compensation text.
min_salary	Number	Parsed lower salary value when detectable.
max_salary	Number	Parsed upper salary value when detectable.
currency	String	Parsed currency code such as USD, EUR, or GBP.
salary_interval	String	Pay period such as year, month, week, day, or hour.
job_type	String	Employment type such as full-time, part-time, contract, or freelance.
skills	Array of String	Skills or keywords published with the listing.
date_posted	String	Source posting date.
expires_at	String	Listing expiry date when published by the source.
description_html	String	Clean formatted job description.
description_text	String	Plain-text version of the job description.
url	String	Direct WeWorkRemotely job listing URL.
guid	String	Source listing identifier.
apply_url	String	Application URL when it can be identified.
company_website	String	Company website when published in the listing.
rss_creator	String	Source creator metadata when available.
_source	String	Source label for traceability.

How to scrape WeWorkRemotely jobs

1. Open this Actor in Apify Console.
2. Select all or a specific job category.
3. Add optional WeWorkRemotely search URLs if you want keyword matching.
4. Set results_wanted to the maximum number of jobs to save.
5. Start the run and review the dataset preview.
6. Download the results or connect the dataset to your hiring, research, or reporting workflow.

The simplest run uses the default all category and returns up to 20 records. Search URLs are optional. When provided, the actor reads the term, q, or query value from each URL and keeps a listing when at least one term matches its searchable job information.

Input Parameters

Parameter	Type	Required	Default	Description
search_urls	Array of String	No	[]	Optional WeWorkRemotely search URLs. Terms from their term, q, or query parameter are used as OR-style keyword filters.
category	String	No	all	Category feed to collect. Use all for the broadest run.
results_wanted	Integer	No	20	Maximum number of unique job records to save. The minimum accepted value is 1.

Supported category values

Value	Category
all	All supported categories
remote-full-stack-programming-jobs	Full-Stack Programming
remote-front-end-programming-jobs	Front-End Programming
remote-back-end-programming-jobs	Back-End Programming
remote-design-jobs	Design
remote-devops-sysadmin-jobs	DevOps and SysAdmin
remote-management-and-finance-jobs	Management and Finance
remote-product-jobs	Product
remote-customer-support-jobs	Customer Support
remote-sales-and-marketing-jobs	Sales and Marketing
all-other-remote-jobs	All Other Remote Jobs

Output Data

The default dataset contains individual job records. Empty source fields are omitted from an item, so not every record will contain every field in the table above.

Usage Examples

Basic remote job collection

Collect up to 20 jobs across all supported WeWorkRemotely categories:

{
  "category": "all",
  "results_wanted": 20
}

Category-based collection

Collect up to 50 back-end programming jobs:

{
  "category": "remote-back-end-programming-jobs",
  "results_wanted": 50
}

Keyword-filtered collection

Collect up to 100 listings matching either python or golang in the supplied search URLs:

{
  "search_urls": [
    "https://weworkremotely.com/remote-jobs/search?term=python",
    "https://weworkremotely.com/remote-jobs/search?term=golang"
  ],
  "category": "all",
  "results_wanted": 100
}

Sample Output

The following is a representative dataset item. Source fields such as salary, skills, and company website appear when the job listing publishes them.

{
  "title": "Senior Backend Engineer",
  "company": "Example Labs",
  "category": "Back-End Programming",
  "categories": [
    "Back-End Programming"
  ],
  "location": "United States",
  "region": "Anywhere in the World",
  "country": "United States",
  "salary": "$120,000 - $160,000 per year",
  "min_salary": 120000,
  "max_salary": 160000,
  "currency": "USD",
  "salary_interval": "year",
  "job_type": "Full-Time",
  "skills": [
    "Node.js",
    "PostgreSQL",
    "AWS"
  ],
  "date_posted": "Fri, 14 Feb 2026 12:31:45 +0000",
  "expires_at": "Sun, 14 Mar 2026 12:31:45 +0000",
  "description_html": "Build and maintain backend systems for a distributed product team.",
  "description_text": "Build and maintain backend systems for a distributed product team.",
  "url": "https://weworkremotely.com/remote-jobs/example-labs-senior-backend-engineer",
  "guid": "https://weworkremotely.com/remote-jobs/example-labs-senior-backend-engineer",
  "apply_url": "https://examplelabs.com/careers/senior-backend-engineer",
  "company_website": "https://examplelabs.com",
  "_source": "weworkremotely-rss-api"
}

Tips for Best Results

• Test with a small limit - Start with results_wanted: 20 to confirm the category and keyword behavior before collecting a larger dataset.
• Use specific search terms - A role, technology, or skill such as data engineer, React, or customer support usually produces a more useful filtered dataset than a broad term.
• Use multiple search URLs for OR matching - Add separate URLs when you want to collect listings matching any of several terms.
• Choose a category for focused analysis - Category runs reduce unrelated records and make comparisons between job areas easier.
• Expect source-dependent fields - Salary, employment type, skills, company website, and expiry date may be missing when the employer did not publish them.
• Review descriptions before analysis - Use description_text for plain-text processing and description_html when basic formatting should be retained.
• Schedule repeat runs for monitoring - Recurring runs can help track new listings, changing compensation information, and category-level hiring patterns.

Integrations and Export Formats

• Google Sheets - Review, filter, and share remote job datasets with a team.
• Airtable - Build a searchable hiring or market research database.
• Slack - Send new job results or monitoring summaries to a channel.
• Webhooks - Notify another service after an Actor run completes.
• Make or Zapier - Route job records into alerts, reports, CRM systems, or other no-code workflows.
• Apify API - Retrieve datasets programmatically and connect scheduled runs to your own application.

Format	Useful for
JSON	Applications, data pipelines, and API workflows
CSV	Spreadsheet review and simple analysis
Excel	Business reports and analyst workflows
XML	Structured data exchange with compatible systems

Frequently Asked Questions

Can I collect more than 20 jobs?

Yes. Set results_wanted to the maximum number of records you want, subject to the listings available in the selected category and your Apify plan.

Can I scrape one WeWorkRemotely job category?

Yes. Set category to one of the supported category values, such as remote-design-jobs or remote-product-jobs. Use all for broad coverage.

Can I filter WeWorkRemotely jobs by keyword?

Yes. Add one or more search URLs to search_urls. The actor reads each URL's term, q, or query value and applies OR-style matching across the listing title, company, category, location, and description.

Why are some salary fields empty?

Salary fields are empty when the public listing does not provide usable compensation information. The original salary field and parsed salary fields are only included when a value can be identified.

Are duplicate job listings removed?

Yes. Listings collected from multiple category feeds are merged by their job URL. Category labels and skills are combined when the same listing provides them in more than one record.

Can I export the results to CSV or Excel?

Yes. Apify datasets support JSON, CSV, Excel, XML, and other export options. You can also connect the dataset to integrations or retrieve it through the Apify API.

Can I schedule recurring runs?

Yes. Use an Apify schedule to run the Actor hourly, daily, weekly, or at another interval that fits your monitoring workflow.

Why is a location set to Remote?

The actor uses Remote when the source description clearly identifies the role as remote but does not provide a more specific usable location. Region, country, and state are retained separately when available.

Is it legal to collect WeWorkRemotely data?

Public-data collection may be subject to laws, privacy requirements, and WeWorkRemotely's terms. You are responsible for using the dataset lawfully, respecting applicable rules, and limiting use to legitimate purposes.

Related Actors

• Jobberman Job Scraper - Collect job listings from Jobberman for recruitment and employment-market research.
• LinkedIn Job Scraper - Collect professional job listings with search, location, and time-range options.
• USA Jobs Scraper - Gather job opportunities focused on the United States employment market.
• Remote Job Scraper - Collect public remote work listings from another job-focused source.

Support

For issues, feature requests, or reports about changed source fields, use the Issues tab on the Actor page or contact the developer through Apify.

Legal Notice

This Actor is intended for legitimate collection of publicly available job information. Users are responsible for complying with applicable laws, website terms, privacy rules, and internal data policies. Use collected job data responsibly and respect reasonable collection limits.