u/Dry-Library-8484

I maintain a open-source job aggregator, so I have every open posting we track sitting in one database along with the date the employer's own ATS says it went live. Got curious how much of the "open" market is actually open.

Out of 4,122,692 open postings that carry a posting date:

• Last 30 days: 1,917,903 (47%)
• 1–3 months: 1,135,345 (28%)
• 3–6 months: 451,559 (11%)
• 6–12 months: 277,681 (7%)
• Over a year: 340,204 (8%)

So roughly 1 in 4 listings you can apply to right now was posted more than three months ago. 1 in 12 has been sitting there over a year.

The year-plus pile is spread across 44,018 different companies. This isn't a handful of spammers, it's normal behavior at a huge number of employers.

Caveats, because I'd rather post this straight:

- The date comes from the employer's ATS. Some systems stamp when the requisition was created and never touch it again, so a role that genuinely got refilled can look ancient.

- A year-old posting isn't automatically fake. Some roles are genuinely hard to fill, and some companies run real evergreen pipelines for high-churn positions.

- This is only postings that expose a date. About 11% don't, and I have no idea what's in there.

None of that changes the shape of it. A quarter of the "open" market is over three months old.

The part that's actually useful. What I check before spending 40 minutes on an application:

1. Open the company's own careers page, not the board listing. If the role isn't there, it's dead. This one catches the most.

2. Same title, same company, posted more than once or reposted every few weeks. That's a pipeline, not an opening.

3. Evergreen phrasing. "We're always looking for", "join our talent pool", "future opportunities" — no headcount behind any of those.

4. Has anything changed since it went up? If the description hasn't moved in a year, nobody owns that req.

What's the oldest posting anyone here actually got an interview from? I want to know if the year-old ones ever convert at all.

Hey I am building job search tool, and wanted to talk with job hunters
It will be fully free, just want you to ask questions and join to community

I live in Brazil and I've been hunting for remote roles in US companies for a while. Every job board I tried either didn't filter for remote-eligible-from-LatAm properly, or was flooded with roles that quietly required US/EU residency once you actually opened them.

So I built freehire — it pulls postings directly from company ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.) instead of scraping other job boards, so the "remote" tag is more reliable. Right now it's tracking around 35k roles relevant to LatAm.

It's open source: github.com/strelov1/freehire.

Still actively working on it — if you try it and something's broken or missing (a filter that doesn't work, a company that should be remote-friendly but isn't showing up), tell me, genuinely useful for me to know

I live in Brazil and I've been hunting for remote roles in US companies for a while. Every job board I tried either didn't filter for remote-eligible-from-LatAm properly, or was flooded with roles that quietly required US/EU residency once you actually opened them.

So I built freehire — it pulls postings directly from company ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.) instead of scraping other job boards, so the "remote" tag is more reliable. Right now it's tracking around 35k roles relevant to LatAm.

It's open source: github.com/strelov1/freehire.

Still actively working on it — if you try it and something's broken or missing (a filter that doesn't work, a company that should be remote-friendly but isn't showing up), tell me, genuinely useful for me to know

I run freehire, an open-source job aggregator that ingests postings from dozens of ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.), normalizes them into one schema, and runs each through a facet pipeline.

Just exported the whole catalogue to Hugging Face: freehire-jobs — 6,041,471 postings.

Each row is a raw posting (title, company, description, URL, source, posted_at) plus derived facets:

- Dictionary-only (deterministic, no LLM): skills, seniority, category, work_mode, posting_language, employment_type, education_level, english_level, experience_years_min

- Dictionary-first, LLM-filled: countries, regions, cities

- LLM-only: salary_min/max, currency, period — pay is stated in every format imaginable, no dictionary handles that

Company rows carry their own facets too (industries, HQ country, size, YC batch/stage where applicable).

Format: 20 gzip JSONL shards, split by row range for easy streaming/resuming.

Backstory: I originally wanted to train a cheap classifier for seniority/category tagging instead of paying for an LLM call per job. Turned out the dictionary+LLM pipeline already in prod beats a from-scratch classifier by enough that finishing the classifier wasn't worth it — so I'm sharing the data instead.