Time-to-fill is the number of calendar days between a requisition being approved and a candidate accepting the offer. It is not the same as time-to-hire (first contact → offer accepted): time-to-fill includes the queue — the days a role sits waiting for recruiter capacity before any candidate is even contacted. In engineering hiring, the queue is usually the largest single component.
Published time-to-fill benchmarks, 2026
What the published data says about how long it takes to fill a role — by category, against the embedded model.
| Role category | Median time-to-fill | Source |
|---|---|---|
| All roles, US average | 44 days | SHRM, 2025 |
| Tech roles, all functions | 48 days | Aggregated ATS benchmark data, 2025 |
| Software engineering | 62 days | Workable global benchmarks, 2025 |
| AI / ML engineering | 89 days | Industry hiring surveys, 2025 |
| Senior & staff-plus engineering | 40% exceed 90 days | LinkedIn Talent Solutions, 2025 |
| NGRS embedded model | 14–28 days (2–4 weeks) | NGRS engagement data, 12 mo to June 2026 |
External figures: published 2025 industry benchmarks as cited per row. NGRS figures: 30+ clients and 400+ positions closed in the 12 months to June 2026.
Why engineering hires take 60–90 days
Three structural forces — none of them is "we can't find people". The bottleneck is almost never sourcing.
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Funnel drop-off compounds
Each stage multiplies: ~20–25% outreach reply, ~50% screen pass, ~50% interview pass, ~50–75% offer accept. To land one senior engineer you typically process 100–200 sourced profiles — and every stage adds calendar days, not just effort.
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Internal capacity collapsed
In-house recruiter headcount is down roughly 23% since 2022 while open requisitions per recruiter are up 56% (Gem Recruiting Benchmarks, 2025). Roles queue for weeks before anyone touches them — queue time is pure time-to-fill with zero progress.
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Decision latency
Panel scheduling, multi-round loops, slow offer approvals. Most of a 62-day fill is waiting, not working — and the best candidates accept competing offers during exactly those waits.
What actually compresses time-to-fill
Capacity on day one. The single biggest lever is removing the queue. A dedicated team that starts sourcing the day the role opens — instead of week three — removes 15–20 days before any process improvement is even discussed. That is why the NGRS embedded model produces a first shortlist within 2 business days: the capacity already exists, calibrated and warm.
A funnel re-architected for parallel volume. Sequential hiring — one role, one loop, one offer at a time — does not scale and does not accelerate. Running many roles through one re-architected funnel is how NGRS closes roughly 27 engineering roles per 4-week cycle, with first offers typically signed in weeks 2–3. The same engine delivered 400 engineers for a Fortune-500 fintech in 24 months (see the case studies).
Stage SLAs instead of best effort. Screen within 48 hours, interview feedback same day, offer within 24 hours of the final round. Companies that put hard SLAs on every funnel stage routinely cut a third of their cycle without touching the quality bar.
Speed does not cost quality — it protects it. The fastest process wins the best candidates, because senior engineers choose the employer that decides first. NGRS placements show 97% retention at 12 months — at a 2–4 week time-to-fill. How that compares to classic agencies and pure in-house hiring is laid out on the comparison page; if you want the math run on your own roles, talk to us.
The same roles, on the embedded clock
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First signal2 business days to first shortlist
Calibrated candidates on your desk before a traditional process has finished writing the job description.
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Per role2–4 weeks typical time-to-fill
Against a 62-day engineering median and 89 days for AI/ML — first offers usually signed in weeks 2–3.
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Quality check97% retention at 12 months
Fast fills that stay. Speed comes from process design, not from lowering the bar.
NGRS figures across 30+ clients and 400+ positions closed in the 12 months to June 2026. Founded 2007 · 110+ consultants.
Common questions about time-to-fill
How long does it take to hire a software engineer in 2026?
About 62 days on average in the US, against a 44-day all-roles average (SHRM, 2025). AI/ML roles run to 89 days, and roughly 40% of senior engineering searches exceed 90 days. Embedded recruitment models like NGRS's typically fill the same roles in 2–4 weeks, with a first shortlist in 2 business days.
What is a good time-to-fill benchmark for tech roles?
Under 48 days beats the 2026 tech median; under 30 days is top-decile and almost always requires dedicated recruiting capacity from day one plus hard SLAs on every funnel stage. If your engineering fills consistently run past 70 days, the process — not the talent market — is the constraint.
Why do engineering roles take so much longer to fill than other jobs?
Three reasons: the best engineers are passive and must be sourced and convinced rather than collected from job boards; multi-stage technical loops add calendar weeks of scheduling; and shrunken in-house recruiting teams — headcount down ~23% since 2022 with 56% more requisitions per recruiter (Gem, 2025) — leave roles queuing before work even starts.
Does hiring faster mean hiring worse?
No — the data points the other way. Speed won through process design (parallel loops, stage SLAs, fast offers) wins the candidates who have competing offers. NGRS fills engineering roles in 2–4 weeks and still shows 97% retention at 12 months across 400+ positions closed in the last year.
How can we get our time-to-fill below 30 days?
Remove the queue (dedicated capacity from day one), run roles in parallel through a funnel built for volume, and enforce SLAs at every stage — screen in 48 hours, offer within 24 hours of the final round. That is the core of the NGRS Surge Hiring method, which closes roughly 27 engineering roles per 4-week cycle.
Want your roles on the 2–4 week clock?
A 30-minute call: bring your open roles and your current time-to-fill. We'll show you where the days are leaking and what an embedded team would change — honestly, including whether you need one at all.