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Got ghosted by tech companies so I built a tool to track ghost jobs

Got ghosted by tech companies so I built a tool to track ghost jobs

by ktmartin·May 16, 2026·7 points·3 comments

AI Analysis

●●SolidDark HorseNiche Gem

Reveals Datadog and Jane Street roles open for 2,500+ days using Greenhouse data.

Strengths
  • Filters specifically for Greenhouse ATS users to ensure consistent date tracking.
  • Exposes specific companies like Appier with roles open for seven years.
  • Offers raw CSV downloads instead of gating data behind an email wall.
Weaknesses
  • Lacks interactive filtering; currently just static tables and blog-style insights.
  • Does not distinguish between reposted roles and genuinely stagnant listings.
Category
Target Audience

Job seekers, recruiters, and labor market analysts

Similar To

Layoffs.fyi · Hacker News Hiring Trends · Glassdoor

Post Description

Last year I was looking for a new role. I sent out applications, did the prep, waited. What came back was mostly nothing. Not rejection emails, just silence. The job listings I'd applied to stayed live for weeks. Some for months.

As a software engineer, I decided to dig into it properly. I built a system to continuously track job postings across companies, logging posting dates and measuring how long roles stay open before closing or don't. After 35,000+ listings across 200+ companies, some patterns are hard to ignore. Some listings have been open for 700+ days at companies you'd recognize. Others post 90% of their open roles within a single month, a signal that's harder to fake than a press release.

I published two initial insight pages based on this work: - Which companies are posting most aggressively right now - Job listings that have been open for over a year

What I didn't expect is that the same signals useful for detecting ghost jobs also say something broader about a company's hiring momentum, recruiting intensity, pipeline health, where talent bottlenecks might exist. I'm not sure yet where this leads, but I'll keep expanding the dataset and publishing more insights as I go.

Would genuinely love feedback on the methodology, interpretation, or obvious blind spots in the data.

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