How we detect job scams
The signals, automated filters, and human-review steps we use to keep fraudulent listings off the site.
Signal 1: Money flowing the wrong way
Real employers pay you. Any listing that requires a candidate to pay for training, equipment, software, "background checks", or visa processing is treated as a scam and removed. We also filter listings that route applications through fee-based "recruiter" intermediaries.
Signal 2: Unverifiable employer identity
We check that the employer has a real online footprint — a website, a LinkedIn page, and consistent contact information. Listings from generic Gmail/Outlook addresses, unmatched company names, or vague "remote agency" descriptions get held for manual review.
Signal 3: Implausible pay or guarantees
"Earn $5,000 a week from home, no experience needed" is not a real job. We apply automated filters to compensation language that's wildly above market for the role plus titles that promise guaranteed earnings, weekly payouts, or instant onboarding.
Signal 4: Pressure tactics and information harvesting
Real recruiters don't ask for your bank details, government ID copies, or full Social Security number before an offer. Listings whose application flow demands sensitive data upfront, or that pressure candidates to commit within hours, are removed.
Signal 5: Visa-sponsorship bait
A category we monitor closely. Listings that promise guaranteed sponsorship without naming a specific visa class, salary threshold, or sponsor entity are treated as suspect. See our guide on visa-sponsorship job scams for the full pattern catalogue.
Reporting workflow
Every job page has a "Report this listing" button. Reports tagged as scam or broken link escalate to high priority automatically; an editor reviews within one business day. Confirmed scams are removed and the source feed is re-examined for similar patterns. Reporters get an acknowledgement and an outcome notification if they leave an email.
Limits of automation
Scammers adapt. No automated filter catches every fraudulent listing, especially newer schemes that mimic legitimate employer language. We rely on a combination of feed-level filters, manual sampling of high-risk categories, and user reports. If something looks wrong, please report it — your reports materially improve filter quality for everyone else.