An original data report from True Jobs · Updated July 2026
Most commentary about the job market relies on what companies say they are hiring for. We can offer a different vantage point: what the listings themselves actually look like once you score every one for legitimacy. The figures below are computed live from the True Jobs dataset of listings aggregated from more than 20 sources and run through our Realness Score. No personal data, no individual listings — just the aggregate picture job seekers rarely get to see.
Before anything reaches this dataset it has already cleared two hurdles: it came from a source we can verify (a real company's ATS board or a vetted aggregator), and it was posted within the last 14 days. Studies put the share of ghost jobs, stale reposts, and outright fakes across the open web as high as 40% — the bands below show what the listing pool looks like after you start from verifiable sources and drop everything stale:
97.9% of scored listings (334) showed strong legitimacy signals — a verifiable employer, a fresh posting, a specific description, and a trustworthy source. 2.1% (7) landed in the mixed band, where a quick independent check is warranted before you invest time. And 0.0% (0) carried weak or concerning signals: stale postings, anonymous "companies," template descriptions, or implausible pay.
The practical takeaway: sourcing is most of the battle. Starting from verifiable career pages and fresh postings filters out the bulk of the junk before a score is ever computed — the score then flags the residue that slips through. If your current search starts from an open job board instead, assume the mix is far worse than the chart above.
Work mode is one of the first filters most job seekers apply, so it is worth knowing whether legitimacy varies across remote, hybrid, and on-site roles in our dataset:
| Work mode | Listings scored | Avg. Realness Score |
|---|---|---|
| Remote | 195 | 77.1 |
| Onsite | 134 | 79.3 |
| Hybrid | 12 | 79.2 |
Differences across work modes tend to reflect who is posting and how: categories that attract more reposted or pipeline-building listings drag their averages down, while roles posted directly by employers to fill an immediate need score higher. Use the averages as context, not as a reason to avoid an entire category — a high-scoring remote role still beats a low-scoring on-site one.
Only 74.5% of scored listings disclosed any salary information. Beyond being a fairness issue, missing pay data is also a mild legitimacy signal: genuine employers filling a real role are increasingly willing to state a range, while pipeline and bait listings rarely bother. When you do see a salary, sanity-check it — pay that is wildly above or below the market for the role is one of the clearest scam indicators we track.
This report regenerates from live data. Read the full Realness Score methodology, learn how to spot fake listings yourself, or browse scored jobs now.