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Q4 2025 · United States
3 hr tech startups based in United States ranked by GitHub engineering acceleration. Filtered from our broader HR Tech sector rankings.
| # | Company | Stage | Geo | Commits (14d) | Change | Contributors | Contrib. Growth | New Repos | Signal |
|---|---|---|---|---|---|---|---|---|---|
| 1 | zapplyjobs Free job boards and career resources for students & new grads View signal profile → | Seed | US | 43 | +999% | 8 | +220% | 0 | Engineering hiring burst |
| 2 | ever-co The Everything Platform for Businesses™ View signal profile → | Growth | US | 29 | +7% | 79 | +0% | 1 | Framework migration |
| 3 | PostHog The single platform to analyze, test, observe, and deploy new features View signal profile → | Growth | US | 739 | +4% | 100 | +0% | 4 | Infrastructure buildout |
Sorted by commit velocity change (14-day window, descending). Data last updated Q4 2025. Geography from GitHub org profiles.
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In Q4 2025, zapplyjobs leads hr tech startups in United States with 43 commits over a 14-day window (+999% change) and 8 active contributors. Across all 3 tracked United States-based startups in this sector, the average 14-day commit velocity is 270 commits. The dominant signal pattern is "Engineering hiring burst", which typically indicates rapid team expansion ahead of a product launch or fundraise.
United States accounts for 3 of the hr tech startups in our tracking dataset for Q4 2025. This geographic view filters the broader sector rankings to help investors focused on United States-based deal flow identify engineering acceleration patterns within their target geography. Regional concentrations often reflect local regulatory environments, talent pools, and investor networks that shape startup trajectories differently from global averages.
We derive startup geography primarily from the GitHub organization profile location field, supplemented by a manually curated enrichment database of known startup headquarters. This means startups without a public GitHub location may appear in our global sector rankings but not in geographic filters. The geographic classification uses broad regions (United States, etc.) rather than city-level granularity to provide meaningful sample sizes for comparison.