Editorial · Ghost jobs · OpenAI
Ghost jobs at OpenAI
Loading repost-cluster analysis for OpenAI…
Repost clusters
Each row is a normalised (title, company) pair appearing more than once in the current corpus. Count is the total number of listings sharing that pair — counting that as one role advertised N times. Tier thresholds: chronic = 5 or more, persistent = 3 to 4, duplicate = 2.
| Role | Count | Tier | First seen | Last seen |
|---|---|---|---|---|
| Loading clusters… | ||||
Data refreshes from the live corpus on every page load. The underlying endpoint is /repost_clusters.php?company=OpenAI — same data, JSON, CORS-enabled.
What this is, and isn't
A repost cluster is observational, not accusatory. There are legitimate reasons the same role appears multiple times — separate req IDs per region, an ATS export that creates duplicate entries, the role was filled and reopened, or two separate teams hire for the same title. We report the counts; we do not issue verdicts.
What the data does show is how often a remote job seeker is asked to evaluate the same opportunity twice. For a candidate applying to twenty roles a week, repost density is a real cost.
Cross-references
- OpenAI hub on RemNavi — industry, ATS, current open roles, RRS distribution.
- Ghost jobs in remote tech — the corpus-level view across all employers.
- Hybrid Transparency Score — companion editorial on hybrid-listing disclosure.
- Corrections log — flag a misclassification or appeal a result.
Cite this page
RemNavi (2026). Repost-cluster analysis: OpenAI. Retrieved 2026-05-01 from https://remnavi.com/editorial/ghost-jobs/openai/.
Press contact for context, comment, or methodology questions: press@remnavi.com.