We Work Remotely Jobs Scraper

Scrape the We Work Remotely feed into a clean JSON or CSV dataset with the company split from the title. Real expiry dates make the ghost-job score evidence-based: expired and stale postings are detected outright, not guessed from age. Salary parsed where published.

We Work Remotely jobs: two things that break naive scrapers

1. There is no company field. We Work Remotely publishes an RSS feed where the employer is glued to the front of the title: "Fullscript: Senior Product Designer". A scraper that maps title straight through gives you a company column that does not exist and a job title that starts with a company name.

This Actor splits them — but only on the first colon, and only when both halves are plausible. Titles legitimately contain colons ("Engineer: Platform Team"), and splitting those would invent a company that does not exist.

2. It publishes real expiry dates. Along with Himalayas, this is one of only two boards here that states when a listing closes. That makes the ghost-job scoring unusually well-grounded: an expired posting is detected outright rather than guessed at from age.

Region coverage is strong too — around 96% of listings resolve to a hiring region, since the feed carries separate region, country and state fields rather than one free-text blob.

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We Work Remotely job data: what each row contains

Identity	stable id across runs, posting URL, direct applyUrl where published
The role	title, company, plain-text description, tags, employment type, seniority
Where	remote flag, the board's own location text, and normalised regions (worldwide / usa / canada / latam / uk / europe / apac / africa / middle_east)
Pay	min, max, currency and period as numbers, annualised on request
When	posted date, expiry where published, age in days
Trust	freshness 0-1, ghostRisk low/medium/high, and ghostReason explaining the verdict in plain words

Ghost job detection: the trust fields

Job boards are full of postings still published but no longer open — filled roles left up for pipeline, evergreen "talent pool" adverts, and listings nothing ever expires. Every row here carries a risk band and the reasons behind it:

{
  "title": "Senior Backend Engineer",
  "company": "Acme",
  "freshness": 0.71,
  "ageDays": 10.4,
  "ghostRisk": "low",
  "ghostReason": ["recent, and nothing contradicts it"]
}

Signals come from what the board actually publishes: age, its own expiry date where there is one, missing application links, and evergreen phrasing. Nothing is inferred by a model. Freshness decays on a 21-day half-life, and a posting with no date scores 0.5 rather than 1.0 — absence of evidence is not evidence of freshness.

Example input: fresh design and product jobs for the US or Canada, titles and links only

{
  "query": ["designer", "product"],
  "regions": ["usa", "canada"],
  "maxAgeDays": 14,
  "maxGhostRisk": "low",
  "includeDescription": false,
  "maxItems": 200
}

Every field is optional — run it empty and you get the whole We Work Remotely feed, best-first.

• Search terms are OR-ed. ["go", "rust"] returns jobs mentioning either.
• Worldwide jobs match every region filter, because a job open to everyone is open to you.
• minSalary is annualised first, so hourly and monthly rates compare correctly.
• maxItems is your cost ceiling — you are billed per job returned.
• maxPagesPerSource: We Work Remotely serves its whole feed in one call, so this setting has no effect for this board.

Output: one dataset item per We Work Remotely job

One dataset item per job, ordered best-first: lowest ghost-job risk, then freshest. Export as JSON, CSV or Excel, or read it from the API like any Apify dataset.

If We Work Remotely itself returns no jobs — the board is down or has changed its format — the run fails with a clear status message instead of succeeding with an empty dataset, so a failed-run alert on a schedule catches it. A run whose filters match nothing still succeeds, with an empty dataset and a status line saying so. When fewer than half of the rows carry a company or a posted date, the run log prints a WARNING.

We Work Remotely jobs scraper pricing: a worked cost example

You pay per job returned, on Apify's pay-per-event model, and nothing for platform usage on top: $0.006 per job on the Free and Bronze plans, $0.003 on Silver, $0.002 on Gold, Platinum and Diamond. Rows dropped by your filters are never billed, and a run that returns nothing costs nothing.

Run	Jobs returned	Free / Bronze	Silver	Gold and above
A run returning 100 jobs	100	$0.60	$0.30	$0.20
A run capped with maxItems: 200 (at most)	200	$1.20	$0.60	$0.40
A run whose filters match nothing	0	$0.00	$0.00	$0.00

maxItems is the hard ceiling: maxItems: 200 can never cost more than $1.20 on the Free plan. Apify's free plan includes a monthly usage credit, so a first run costs nothing out of pocket.

Use cases for We Work Remotely job data

• Filtering out expired and stale job postings. This board publishes real expiry dates, so ghostRisk, ghostReason and freshness are unusually well grounded here — an expired posting is detected outright rather than inferred from age.
• Job posting freshness checks. If your question is "is this listing still open?", this is the board where the answer is evidence-based.
• Salary benchmarking and compensation data. Where this board publishes pay, salaryMin, salaryMax, salaryCurrency and salaryPeriod come through as numbers, and minSalary annualises before comparing.
• Job market and hiring data. Run it on a schedule and track how hiring, salary ranges, seniority mix and regions move over time.
• Building a job board, job feed or job alerts. id is stable across runs, so diffing today's dataset against yesterday's gives you genuinely new jobs rather than a board reshuffle.
• Recruitment and talent research. Company, title, tags, seniority, employment type and normalised regions on every row.

Export as JSON, CSV or Excel, or read the dataset straight from the Apify API.

How this We Work Remotely scraper fetches jobs

We Work Remotely publishes a public RSS feed and this Actor reads it — paced, budgeted, with an honest user agent. No HTML scraping, no bot-check evasion, no personal data. Job adverts only.

How to scrape We Work Remotely jobs with this Actor

1. Open the Actor and press Start. With no input it returns the whole We Work Remotely feed, best-first.
2. Narrow it when you need to: search terms, hiring regions, seniority, a minimum salary, a maximum ghost-job risk, and maxItems as a hard cost ceiling.
3. Export the dataset as JSON, CSV or Excel, read it through the Apify API, or schedule the run and attach a webhook or an integration (Google Sheets, Make, Zapier, Slack) so new We Work Remotely jobs arrive on their own.

Related Actors from the same pipeline

• Remote Jobs Aggregator — six boards in one call, deduplicated, with salary parsing and ghost-job scoring
• Himalayas Remote Jobs Scraper — the board with real expiry dates and numeric timezone restrictions
• RemoteOK Remote Jobs Scraper — genuinely remote developer jobs, the on-site leakage filtered out
• Remotive Jobs Scraper — a small, hand-curated board of remote tech jobs
• Arbeitnow Jobs Scraper — Germany and Europe, remote and on-site, the largest single feed
• Jobicy Remote Jobs Scraper — the cleanest structured salary data for benchmarking
• Dice Jobs Scraper — US tech jobs with salary parsed to the cent

All eight share one schema, one ghost-job model and one set of filters, so a query written for this board runs unchanged against any of the others.

FAQ: scraping We Work Remotely jobs

Does We Work Remotely have an official API, and is it legal to scrape We Work Remotely jobs? We Work Remotely publishes a public RSS feed and this Actor reads it — paced, budgeted, with an honest user agent. No HTML scraping, no bot-check evasion, no personal data. Job adverts only.

How many We Work Remotely jobs can one run return? The whole RSS feed, which arrives in one call, so maxPagesPerSource has no effect here; maxItems caps it when you only need the newest listings.

How often should I run this We Work Remotely scraper, and can I get only the new jobs? A daily schedule keeps the dataset current. id is stable across runs, so diffing today's dataset against yesterday's yields only the genuinely new We Work Remotely jobs, not a reshuffle; maxAgeDays set to 1 or 2 is the cheaper version of the same idea.

How do I get only fresh, still-open We Work Remotely jobs? Set maxGhostRisk to low and maxAgeDays to 14 or 21. Every row also carries ghostReason, the plain-language grounds for its rating, so you can apply your own rule instead.

What is a ghost job? A posting that is still published but no longer open: a filled role left up for pipeline, an evergreen "talent pool" advert, or a listing on a board that never expires anything. The ghostRisk band and freshness score flag them from what the board itself publishes — age, expiry dates, missing application links, evergreen phrasing — never from a model's guess.

Does it include salary data? Only where We Work Remotely publishes it, which is uncommon; those rows carry the parsed numbers in salaryMin, salaryMax, salaryCurrency and salaryPeriod, and salaryOnly keeps just them.

Do I need proxies, a login or an API key? No. We Work Remotely is read the way it asks to be read, and Apify runs the Actor for you; nothing to install and no credentials to manage.

Can I export We Work Remotely jobs to CSV, Excel or Google Sheets? Yes. Every run's dataset downloads as JSON, CSV or Excel from the Apify Console or API, and the Google Sheets integration writes rows straight into a sheet.

Is this a remote job scraper I can run as an API? Yes. Apify runs every Actor from its API as well as the Console: start a run over HTTP with the same JSON input shown above, or put it on a schedule, then read the finished dataset as JSON, CSV or Excel. The dataset is the output; there is no separate query endpoint, so a scheduled run plus a dataset read is the API.

Can an AI agent use this We Work Remotely scraper? Yes, through Apify's own MCP server, which exposes Store Actors as tools to Claude and other MCP clients. Nothing here is agent-specific: the agent sends the same JSON input and reads the same dataset.

How do I drop expired job listings? Set maxGhostRisk to low or medium and they are dropped before they are returned, so you do not pay for them. Every row that is returned carries ghostRisk (low, medium or high), ghostReason (the plain-language grounds, such as "board's own expiry date passed 3 days ago" or "posted 64 days ago") and freshness (0-1, decaying on a 21-day half-life, 0 once a published expiry date has passed), so you can apply your own rule after export instead. maxAgeDays drops anything older than N days, including rows with no posted date.

What does a We Work Remotely job row not contain? No visa-sponsorship flag, no applicant counts, no recruiter emails or any personal data, and no company size — We Work Remotely does not publish them in its feed, so they are not invented here. A blank seniority means the board never said; a blank salary means unpublished, not zero.

Want more than one board?

Remote Jobs Aggregator runs this same pipeline across six boards at once — Remote OK, Remotive, Himalayas, Arbeitnow, We Work Remotely and Jobicy — folding duplicates and recording which boards carry each job.

Measured while building it: those six boards barely overlap. Only 1 of 1,513 company+title pairs appeared on more than one. The aggregator is not about removing duplication — there is almost none — it is about getting six boards' worth of distinct jobs from one call.