10 Years of IT Recruiting: The 5 Mistakes Companies Still Make When Hiring Engineers

Since 2016, IT recruiting agency EvoTalents has filled 400+ engineering roles across product companies, IT service firms, and funded startups. Over that time, the same failure patterns appear with remarkable consistency - regardless of company size, tech stack, or industry vertical.

In the past month, we ran structured discovery calls with 12 companies actively hiring engineers. Eight of those 12 had internal HR teams without meaningful IT expertise. Seven had already failed to fill niche technical roles. Five had budget expectations that were misaligned with current market rates. Five more carried visible distrust from prior agency experiences that had delivered the wrong candidates or none at all.

The problems are not unique. They repeat. And understanding them before you open a role - rather than after three months of failed searches - is the difference between a filled position and an empty chair that slows your roadmap.

Here are the five mistakes we see most often. And the sixth that emerged in 2026.

Mistake 1: Treating IT Hiring Like General Hiring

General HR processes were designed for volume hiring with standardized roles. Engineering hiring operates on completely different logic. The talent pool is smaller, the evaluation criteria are technical, and the candidates who matter most are already employed - often happily.

The result of applying general hiring practices to technical roles: job postings on general job boards that the target audience ignores, screening calls conducted by people who cannot evaluate technical claims, and success metrics borrowed from non-technical recruitment (time-to-post, application volume) that have no bearing on quality outcomes.

Eight of the twelve companies we spoke with in recent months had in-house HR teams handling technical roles without IT-specific knowledge. In every case, the screening stage was the bottleneck: either strong candidates passed unrecognized, or technically weak candidates advanced because the screener lacked reference points to assess them.

Fixing this does not necessarily mean replacing your HR team. It means either upskilling them specifically for technical screening, pairing them with a hiring manager for early-stage calls, or partnering with a specialized function that handles IT roles as a core competency - not a side practice.

Mistake 2: Writing "Unicorn" Job Descriptions That Filter Out Every Real Candidate

The unicorn job description is one of the most common and self-defeating patterns in tech hiring. It combines the requirements of three separate senior roles, lists every technology the team has ever touched, asks for 8+ years of experience in a framework that has existed for 4, and then wonders why the candidate pool is empty.

Seven of twelve companies in our recent discovery calls had open roles that fit this pattern - positions that had been open for two or more months because the requirement set was structurally impossible to fulfill.

The underlying cause is usually a legitimate need that got distorted in translation. A hiring manager has a concrete gap on the team - a missing skill, an underserved function - and during the job description drafting process, that gap expands to incorporate every adjacent wish. The resulting document does not describe a person who exists. It describes an ideal that filters out every real candidate.

A practical fix: force a distinction between must-have and nice-to-have, and be honest about whether a candidate who meets 100 percent of the must-haves but only 60 percent of the nice-to-haves would actually be hired. If the answer is yes, the job description should reflect that.

Mistake 3: Setting a Budget Below Market - and Being Surprised When Candidates Drop at Offer

Five of the twelve companies we spoke with had compensation ranges that were 20 to 40 percent below market for senior roles. In every case, this became visible only at the offer stage - after weeks of sourcing, screening, and interviews had already been completed.

The experienced engineering talent market - particularly in distributed systems, security, AI/ML infrastructure, or specialized stacks like Rust and Solana - operates on real, current prices. Data from two years ago does not reflect what candidates are accepting today. And the candidates who attract the most competing offers know precisely what they are worth.

The cost of a budget gap is not just a lost candidate. It is recruiter hours, hiring manager time, delayed project timelines, and a reputation hit in a technical community that is much smaller and more interconnected than it appears. Recovering a brand associated with below-market offers takes time.

The right sequence: validate compensation against current market data before the role is posted, not after the first offer is declined. The Stack Overflow Developer Survey and comparable recent placements in similar markets provide more current benchmarks than internal salary bands that have not been reviewed in two years.

Mistake 4: Starting the Search Too Late

One of the most consistent timing errors in engineering hiring is the gap between when a need becomes urgent and when sourcing actually begins. A company decides in April that it needs a senior backend engineer by July. Sourcing starts in early July. Then it becomes clear that strong candidates need 3 to 6 weeks just to complete final interview stages and work notice periods at their current employer. The role closes in October, if at all.

For niche engineering roles - security engineers, ML engineers, senior embedded engineers, positions requiring rare combinations of technical stack and domain experience - a realistic time-to-fill for a quality search is 8 to 16 weeks. That assumes a well-scoped role, competitive compensation, and an immediate start.

The implication is direct: if a hire is needed by Q3, the search should start in Q2. Waiting for official headcount approval is not necessary. Market mapping, early pipeline development, and initial candidate conversations can begin before that gate.

Starting 4 weeks late typically means finishing 6 to 8 weeks late, because the second and third candidate rounds are never as strong as the first.

Mistake 5: Blaming the Agency When the Problem Is Internal

Five of the twelve companies we spoke with expressed real distrust of external agencies - and every one of them had a specific story behind it: candidates who did not match the brief, a researcher who disappeared after a retainer payment, a placement that left within 90 days.

That distrust is often earned. There are many generalist agencies that take on technical roles without understanding the stack and substitute volume for quality. Bad agency experiences are common.

But there is another side. Some of the most persistent hiring problems we observe are ones that no agency can solve: onboarding experiences that cause new hires to leave within three months, roles that change scope after the offer is signed, cultural mismatches that were visible during interviews and were ignored, or feedback loops so slow that a strong candidate accepts another offer while waiting for a second round.

Understanding which category a problem belongs to determines what actually needs to change. Wrong candidates from the agency means a different agency or a better brief. A new hire who leaves in two months means an internal problem: the role, the onboarding, the management. Conflating these two things means the real problem never gets addressed.

Mistake 6 (New in 2026): Buying AI Tools Without Fixing the Process First

A new category of mistake emerged in 2024 and 2025, at the intersection of AI enthusiasm and recruiting operations. Companies began allocating $30,000 to $70,000 toward AI-powered recruiting platforms - automated sourcing, resume parsing, candidate scoring, interview scheduling bots - without first diagnosing the process problems those tools were supposed to solve.

The results were predictable. AI applied to a broken process produces broken output, faster. A sourcing tool fed a poorly written job description generates more irrelevant candidates. A scoring model trained on historical hiring data reproduces every existing bias at scale. An automated scheduling system accelerates a process that should have been redesigned before it was automated.

The companies getting real ROI from AI in recruiting in 2026 are the ones that used the platform purchase decision as a forcing function to fix the underlying process first: clarifying role requirements, cleaning up ATS data, benchmarking compensation, standardizing evaluation criteria. Then the tool genuinely accelerates something that was already working.

A useful test before buying any AI recruiting platform: if you removed the AI and did everything manually, would the output be good? If no, the AI will not make it good.

What to Do Before You Open the Next Role

The six mistakes above share a common timing: they are all easiest to address before sourcing begins, not after the first candidate has already declined an offer or accepted a competing one.

We run free 30-minute calls where we work through your specific situation: how the role is scoped, whether the budget is realistic, who is running the screening and how, whether the process is set up to compete for the candidates you actually want. No pitch - just a diagnostic. By the end of the call, you will have a clear picture of where the gap is and what to do about it.