How long does a job posting stay open?

"How long does a job posting stay open?" is one of the most asked questions in job search and one of the most poorly answered. The numbers you read online are almost always industry-averaged, which means they're dominated by whichever sector contributed the most data. Usually retail, hospitality and entry-level operations - which has a very different shape from knowledge work.

We took a snapshot of our live job index on 14 May 2026: a large set of postings monitored directly from company careers pages and from public job boards across nine countries. The company-page side covers every major applicant tracking system - Workday, Greenhouse, Lever, Ashby, Phenom, iCIMS, SmartRecruiters and many more. Every posting in the index carries a timestamp recording when we first observed it. The age of any currently-visible posting is just now minus that timestamp, and that's the distribution this piece is about.

One important caveat up front. This is a snapshot statistic: it measures how long the postings currently visible have been live, not lifecycle time-to-fill from open to close. The two aren't identical. Snapshot age slightly overestimates lifecycle age (long-lived postings are more likely to still be visible) and we cap at around 3 weeks because our index drops anything we haven't re-observed for that long. Practically, the snapshot age is the right answer to the question an applicant actually cares about: if I look at the jobs I can apply to today, how old are they?.

Headline numbers

From the 14 May 2026 snapshot:

• Median age: 9.6 days
• Mean: 8.8 days
• 25th percentile: 5.1 days
• 75th percentile: 12.4 days
• 95th percentile: 17.8 days
• Maximum observed: 23.4 days (the cache drops anything older)

Put differently: of the 835k roles you could see in our feed right now, 7% were posted in the last 24 hours, 20% in the last 3 days, 33% in the last 7, and 89% in the last 14. The "long tail of months-old roles" most job-search advice warns about doesn't really show up in our data - the bulk of what applicants can see is between a few days and two weeks old.

By industry

This is where we expected the data to break apart by sector, and where the headline finding surprised us. Most industries cluster in a narrow band of 7-12 days median age. Median visible age, ordered fastest to slowest, across industries with stable samples (small or recently-added tags excluded):

The fastest sector (retail at 7.1 days) and the slowest (education, HR, data, product, legal at 11.4 days) are only 1.6x apart. That's a much narrower spread than the conventional "high-volume roles fill in days, senior roles take months" framing suggests. A few takeaways from this:

• The "fill in days" advice is mostly about cabin crew, hospitality and ground services - none of which dominate our cache the way they dominate the recruiter-platform datasets that produce headline averages.
• Senior, specialised, and academic roles that take months to fill ARE in the underlying job market, but they're a small fraction of total volume and many of them get re-listed inside our retention window, which compresses their visible age.
• For the vast bulk of knowledge work, the answer to "how urgently should I apply?" doesn't depend much on which industry you're in. It's a roughly week-and-a-half window everywhere.

Why a relatively flat curve matters for applicants

If most roles are 5-12 days old when you see them, two things follow.

First, the "we measured 30-day time-to-fill across the dataset" averages you sometimes see in recruitment-platform blog posts are not what an applicant experiences. From the applicant's seat, the average role you can apply to today is closer to a week and a half old. The 30-day number includes the recruiter's internal interview cycle (which happens after you've already submitted) and is irrelevant to your timing decisions.

Second, the day on which you find a role really does matter. If the median posting is 9.6 days old when applicants see it, applying within 24 hours of it appearing means being on the early side of the bell curve - applicant #5 to #30 territory. Applying a week later means being on the late side - applicant #80 to #150. The recruiter-response-rate data on what happens between those two ranks is sharp.

Direct vs aggregated sources

Worth noting that the median age is not the same across channels. When we split the dataset between roles we monitor directly from company careers pages versus roles picked up from public job boards, the direct-monitoring side runs measurably younger - roughly a 3-day gap at the median. The reason is mechanical: companies pull listings the moment a hire goes through, while government and public boards typically keep them up until a fixed close date, regardless of whether the role is still actually being filled. If you want genuinely fresh roles, the company-page channel is structurally better.

What this means for aggregator-driven job search

If the typical role you can see is 9-10 days old in our direct view of the careers page, and aggregators add another 1.5-3 days of indexing delay on top of that, the role you eventually see on LinkedIn or Indeed is often closer to 12-14 days old. Our companion piece on the ATS-to-LinkedIn delay walks through how that delay arises. For roles where the funnel converges in under 2 weeks (most operational and mid-level knowledge work, by the data above), that's the difference between being in the early-applicant batch and being in the third or fourth wave. The cost analysis covers the practical math.

Methodology and caveats

Snapshot taken on 14 May 2026 from our live job index: a large set of unique postings deduplicated across direct company-page monitoring and public job boards in nine countries. Age is computed as now minus the timestamp recorded the first time we observed each posting. That timestamp is carried forward across monitoring runs, so re-runs don't reset it.

Five things to keep in mind when reading these numbers:

• Snapshot, not lifecycle. We're measuring the age of currently-visible postings, not the lifecycle (open-to-close) time-to-fill. Snapshot age slightly overestimates lifecycle age due to the inspection paradox: long-lived postings are more likely to still be alive in any given snapshot.
• Retention ceiling. Our index drops postings we haven't re-observed for around three weeks, which is why the 95th percentile sits at 17.8 days and the maximum at 23.4. Genuinely long-lived roles (academic searches, senior leadership) are under-represented because they exit our view before they're truly filled.
• "Disappearance" is ambiguous. A role leaving our index could mean filled, withdrawn, re-categorised, or moved to a different URL. We can't distinguish these externally.
• First-observed is when we saw it, not when the employer posted it. For employers we've been monitoring for months, this is essentially the same thing. For employers we've added recently, our first-observed timestamp may post-date the role's actual publication.
• Industry segmentation uses our rule-based tag mapping, which occasionally misclassifies. We excluded industries whose tags were added or substantially expanded inside the retention window (earth sciences, energy, generic "engineering"), since their distributions are dominated by tag-rollout artefacts rather than real role churn.

What this changes in practice

The interesting finding here is the flatness. We expected to see a strong industry signal - aviation closing in 5 days, executive search dragging on for 60 - and instead got a 7-to-11-day window across almost every knowledge-work category. The honest read is that, from the applicant's seat, most roles are visible for about a week and a half, then disappear. The "is this role still hot?" question has roughly the same answer regardless of sector.

Which simplifies the strategy considerably. Same-day application is high-leverage almost everywhere we measured. Our complete guide to applying early covers the routine; the data above is the reason it works.

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