How Long Does It Take to Fill a Tech Role in 2026?

How Long Does It Take to Fill a Tech Role in 2026?

Last updated: April 23, 2026

KORE1’s average time-to-fill for IT and tech roles is 17 days. The SHRM 2025 Recruiting Benchmarking Report puts the U.S. average at 44 days across all positions; tech-specific searches run 48 to 89 days depending on role and seniority. That 3-to-5x gap between internal recruiting timelines and what a specialized agency delivers is why companies end up calling us. Not always by choice. Usually after a 60-day vacancy and a conversation with their CFO about what that open req actually cost them.

Tom Kenaley here. I run technical placements at KORE1, which means most of my week is conversations with hiring managers and HR leaders who are mid-search and starting to feel the drag. The question I get more than any other isn’t about salary benchmarks or candidate quality. It’s: “How long is this going to take?” Usually in that particular tired tone that means they’ve been asking it internally for six weeks and haven’t gotten a straight answer.

This post gives you the honest benchmarks by role and hiring model, with the caveats that make them actually useful. KORE1 works across 30-plus U.S. metros and our IT staffing practice places everyone from frontend developers to AI/ML engineers, so I’ll note where our numbers diverge from published averages and why. Bias disclosed: we benefit when companies can’t hire fast enough on their own.

Why the “Average” Number Doesn’t Help You

The 44-day average from SHRM’s 2025 Recruiting Benchmarking Report covers warehouse coordinators, healthcare administrators, retail managers, and software engineers in the same calculation. That figure isn’t useless. It just describes a different job market than yours.

Pull tech out specifically and the picture shifts. The median for technology positions runs around 48 days. But that’s still blended across frontend engineers and AI/ML specialists, which are different hiring problems entirely. Add the senior-level premium, roughly 20% longer at every level, and a lot of serious tech searches are well above 60 days before you’ve done anything wrong.

Enterprise companies with 1,000-plus employees average 55 days. Startups under 200 people close around 38. The approval chain difference is real. At a startup, a VP makes the call on Friday and the offer goes out Monday. At a 5,000-person company, the process runs through the hiring manager, their director, HR, legal review, comp band approval, and often a panel interview that takes two weeks just to schedule. None of that is a dysfunction. It’s how large organizations manage risk. But if you’re filling a cloud architect in Charlotte and your internal process has eight stages, you’re operating on a different calendar than your competitors.

Time-to-Fill by Tech Role: 2026 Benchmarks

The breakdown below combines industry benchmark data with KORE1 placement patterns across markets including Los Angeles, Dallas, Seattle, Atlanta, and the Irvine and Orange County corridor.

The AI/ML number is worth sitting with. Eighty-nine days is almost three months for a single hire. The pool of engineers who can build and deploy production LLM pipelines, with actual fine-tuning and RAG architecture experience rather than just PyTorch familiarity, is still small relative to the demand coming out of enterprise AI teams. Most of those candidates aren’t responding to cold InMails. They’re already employed and they get a dozen outreaches a week. Generic messages from firms they’ve never heard of go unread.

The Two Bottlenecks Nobody Wants to Say Out Loud

The obvious explanations for slow fills are comp band misalignment and scheduling friction. Those are real. But the bottleneck I see most often in internal recruiting is the pipeline, not the process.

LinkedIn caps recruiter accounts at roughly 40 to 50 meaningful actions per day before the platform flags the activity. Profile visits, InMails, connection requests: the weekly ceiling is low enough that a recruiter managing 15-plus open reqs can only give each role a handful of daily touches. For senior tech roles where the candidates are passive, that ceiling is a real constraint. You’re not going to find the right DevOps engineer in the Bellevue-Redmond corridor by sending 30 InMails that say “exciting opportunity in a fast-paced environment.”

The second bottleneck is hit rate, which is a different problem than volume. Our teams run 5 to 10 AI-augmented outreaches on a tech search and consistently outperform internal teams sending 20-plus generic messages. The difference is specificity. A message built around what a candidate actually built at their last job, referencing their GitHub contributions, their specific Kubernetes version experience, the scale of the data pipeline they shipped, gets a response. A template doesn’t. The LinkedIn ceiling matters less when the messages are working.

Contract-to-Hire: A Different Timeline, a Different Kind of Certainty

Most of the conversation about time-to-fill is really about direct hire. CTH runs differently and deserves its own framing.

With a contract-to-hire arrangement, a qualified candidate can be working within days of approval. Sometimes inside a week if both sides move. The conversion evaluation happens in production rather than across a 5-hour interview loop. You’re watching someone actually work on your codebase, in your environment, with your team dynamics, instead of asking them to whiteboard a distributed systems problem under artificial conditions.

The tradeoff is that a CTH candidate is evaluating you as much as you’re evaluating them. That’s actually useful information. KORE1’s CTH model uses a sliding-fee structure tied to conversion timing. The earlier the conversion, the lower the total fee. It lines up our interests with yours: fast placement, fast conversion, lower cost. The pattern we see consistently is that CTH placements which are genuinely good fits convert within the first 90 days. The ones that don’t were going to be a problem either way, and finding that out at week 12 of a contract is considerably cheaper than finding it out at month 18 of a direct hire with a 6-month guarantee clause.

For roles above $130K where scope or team fit carries real uncertainty, CTH is often the smarter structure. Certainty is expensive when you buy it before you’ve earned it.

What the Vacancy Is Actually Costing You

An unfilled tech role runs approximately $500 per day in lost productivity. That’s fully-loaded salary divided by working days, plus the overhead cost of work that doesn’t get done or gets deferred onto people who were already at capacity.

At the industry-average 48-day fill for a backend engineer: $24,000 in vacancy cost before the recruiter ever gets involved. A 60-day DevOps search runs $30,000. A 90-day AI/ML specialist search: $45,000 in deferred output, not counting what it costs when the AI initiative tied to that role slips into Q4 or gets killed entirely because the team couldn’t staff it.

One of our clients, a healthcare IT company in Irvine that had been carrying an open Snowflake data engineer req for four months, came to us after two analytics projects had already stalled waiting on that hire. The total vacancy cost by the time we placed the role was higher than the fee they paid us. Not marginally higher. Substantially. We’ve written about this dynamic in detail in The Cost of a Bad AI Hire, which covers what slow or wrong hires actually do to a technical organization beyond the obvious recruiting expense.

What HR Managers Ask About Tech Fill Times

What’s actually achievable using a specialized agency versus going internal?

17 days average for KORE1 IT placements, measured from kickoff call to accepted offer or start date. That covers contract, CTH, and direct hire across our markets. Senior roles and AI/ML searches run longer, closer to 3 to 5 weeks, and we’re honest about that upfront. The gap between that and the industry average of 48 to 89 days comes from warm candidate pipelines, not faster process stages. We’re not moving faster through your interview loop. We’re showing up with candidates who fit before your LinkedIn search returns anything useful.

Does the company’s size really move the needle on fill time?

More than most teams account for when they’re planning hiring timelines. The 45% gap between enterprise (55 days) and startup (38 days) is almost entirely approval chains and interview scheduling. We’ve placed candidates at both ends. The offer-extension stage alone at a large financial services firm sometimes takes longer than the entire recruiting process at a 50-person Series B. You can’t fix internal bureaucracy from the recruiting side. The only lever is compressing everything in the stages you control.

AI/ML roles really take 89 days? That can’t be right for good agencies.

The 89-day industry average is real and reflects what happens when companies source cold for this role. The pool of engineers with production LLM experience, whether that’s fine-tuning, RAG architecture, or inference optimization at scale, is genuinely small. Most of them aren’t active candidates. Our AI/ML engineer staffing practice maintains warm relationships in this space specifically because cold outreach doesn’t work here. Even with that, our AI/ML searches run longer than our median. If you’re at a company without those relationships, budget 3 months and plan to flex on one or two requirements or you’ll still be searching in Q3.

Time-to-fill vs. time-to-hire: does the distinction actually matter?

Time-to-fill measures from when the req opens to when it’s closed. Time-to-hire measures from first candidate contact to offer accepted. The first number is what your CFO cares about when calculating vacancy cost. The second is what your recruiting team can actually optimize. Most companies track neither consistently. What matters in practice: how many days is this role open, what does a day of vacancy cost, and what is the fastest credible path to closing it.

Do we build a talent pipeline or just fill the immediate opening?

Fill the opening first. A reactive search on an open req has urgency that pipeline-building can’t match. Every extra day costs $500. Get the role filled. Then, once that person is 30 days in and stable, have the separate conversation about building proactive pipelines for the next 3 roles you’ll open in this stack. Trying to do both at once usually means doing neither. We’ve written about the pipeline-first approach separately and it’s worth reading if you’re planning your Q3 and Q4 tech headcount.

The Part Worth Saying Directly

Most companies don’t know their actual time-to-fill number. They know when the req opened and when the person started, but nobody has run the math on what the vacancy cost, what the recruiting overhead cost, and whether the total outcome justified the process. They should.

If you’re carrying an open tech req and the internal search is past 45 days, the math on specialized recruiting is probably in your favor. Not because agencies are a shortcut to quality, though they’re not, but because 15-plus years of average recruiter experience in a specific stack, warm candidate relationships, and AI-augmented sourcing move faster than a general ATS and a LinkedIn Recruiter seat when the market is competitive.

If that describes where you are, reach out to our IT staffing team. We’ll tell you in the first 20 minutes whether we can close it faster than your current approach or whether you’re actually fine.