New York Beats DC and the Bay Area Combined. Columbus Beats Seattle and Austin.

Counts, not percentages, so nothing here is scaled to a denominator you cannot see. These are rows in a 30,000-posting sample of the matcher corpus, of which 8,193 (27.3%) resolve to a named metro. They are NOT counts over the 239,806-posting active corpus, and the sample is source-skewed in a way the table below makes explicit.

The corpus columns are full-corpus counts over the jobs table and sum to exactly 239,806 across all 20 sources. The sample columns are the 30,000-row matcher cache the metro figures on this page are computed over, and sum to exactly 30,000 across all sources present. himalayas is highlighted because it is the sharpest inversion: 43.0% of the live corpus and 3.5% of the sample. Four sources holding 42,584 live postings between them contribute zero rows to the sample. Snapshot 2026-08-13 03:22 UTC.

Source: Seeker job corpus, 30,000 active job postings, snapshot 2026-08-12. seekerscore.com/insights/job-market-metro-concentration-2026

The ranking, stated with its denominator

Among the 8,193 rows in the sample that resolve to a named metro: New York 2,092 (25.5%), Washington DC 696 (8.5%), San Francisco Bay Area 693 (8.5%), Dallas 559 (6.8%), Philadelphia 371 (4.5%), Chicago 362 (4.4%), Columbus 360 (4.4%), Houston 282 (3.4%), Seattle 243 (3.0%), Los Angeles 229 (2.8%). The top ten metros account for 5,887 of the 8,193 metro-tagged rows, 71.9%. Read against the whole 30,000-row sample rather than the metro-tagged subset, New York is 7.0% and Columbus is 1.2%. Both denominators are correct and they mean different things, which is why we are giving you both instead of picking the flattering one.

The two results that invert the usual map

First, New York is not merely ahead, it is larger than the two cities the tech-career conversation revolves around put together: 2,092 against Washington DC's 696 plus the Bay Area's 693, a ratio of 1.51 to 1. Second, Columbus at 360 outranks Seattle at 243 and is 2.63 times Austin at 137. Philadelphia at 371 also outranks all three. If you have been treating Austin and Seattle as the high-volume destinations and Columbus and Philadelphia as backwaters, this sample says the opposite, at least for the kind of employer it is made of.

Why the sample cannot be read as the market

The metro figures come from a 30,000-row matcher cache, not from the 239,806-posting active corpus, and the cache is not a random draw. Workday is 52.5% of the sample against 15.7% of the corpus. Oracle Cloud is 29.2% against 6.3%. Together those two enterprise applicant-tracking systems are 81.7% of the sample and 22.0% of the corpus. Going the other way, himalayas is 43.0% of the live corpus and 3.5% of the sample, and greenhouse, adzuna, reed and usajobs hold 42,584 live postings between them while contributing zero rows. So this is a picture of where large enterprises running Workday and Oracle post jobs. That is a real and useful population, and it is not the same thing as the labour market.

One employer is large enough to move the ranking

The largest single employer in the sample, JPMorgan Chase, accounts for 6,834 of the 30,000 rows, 22.8%. The top twenty employers account for 22,616 rows, 75.4%. A bank with major hubs in New York, Columbus and the Dallas area is therefore in a position to shape exactly the three results this page leads with. We have not decomposed the metro counts by employer, so we cannot tell you how much of Columbus is one bank. Until someone does that, treat 'Columbus beats Austin' as a claim about this sample's employer mix rather than about the Ohio labour market, and treat the employer concentration as the first thing to check if the finding surprises you.

What we are not telling you about remote work

The same sample reports that 3.0% of postings are remote. We are not publishing that as a market figure, because the sampling above makes it meaningless in that direction. himalayas, a remote-focused board, is 43.0% of the live corpus and 3.5% of the sample, while Workday and Oracle Cloud enterprise feeds dominate what got sampled. A remote rate computed on a sample that systematically excludes the remote-heavy source measures the sample, not the world. The only defensible remote-adjacent statement we can make is a supply-side one: 103,070 of the 239,806 active postings in our corpus (43.0%) come from a remote-focused board. That is a fact about our sources, not an estimate of the share of jobs that are remote.

How to use this if you are deciding where to look

Use it for the direction, not the magnitude. The reliable takeaway is that the volume map for large-enterprise hiring does not match the tech-media map: New York is far larger than the Bay Area, and mid-size Midwest and Northeast metros like Columbus and Philadelphia carry more listings than the cities that get written about. The unreliable part is any precise share, because the denominator is a skewed 30,000-row sample and 72.7% of it carries no metro at all. If you are choosing between two cities, this page is a reason to add Columbus, Philadelphia and Dallas to a list you were about to fill with Austin and Seattle. It is not a reason to move.

How we measured it, and what we checked

Metro counts come from the 30,000-row matcher cache sample of our corpus, snapshot 2026-08-13 03:22 UTC. We verified the denominator arithmetically rather than trusting a label: the per-source sample counts sum to exactly 30,000, and the per-source corpus counts sum to exactly 239,806, which is how we know which figures on this page are sample-scoped and which are full-corpus. Metros are assigned by matching a posting's location against a fixed list of named metros and their constituent cities, so a city absent from that list cannot appear in this ranking no matter how many jobs it has, and 21,807 of the 30,000 rows (72.7%) matched nothing on it. Those unmatched rows include remote postings, country-level locations and cities outside the list, and we have not decomposed them. Every metro percentage on this page is stated against one of two denominators, the 8,193 metro-tagged rows or the full 30,000-row sample, and never against the 239,806-posting corpus, because it was never computed over it.

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