Is Your Technical Hiring Process Screening Out Great Engineers?
The short answer: Many technical hiring processes are doing the latter. High-friction interview loops, live coding under observation, and AI-restrictive assessments are measuring interview performance, not job performance. The fix isn’t rebuilding from scratch — it’s auditing whether your process measures engineering capability or something else entirely.
If you attended our recent webinar with CoderPad CEO Amanda Richardson and talent transformation expert Crystal Miller Lay, you heard a challenge that should make every hiring leader uncomfortable:
What if your technical hiring process isn’t identifying the best engineers? What if it’s identifying the people who are best at interviewing?
For years, technical hiring teams have been told that rigor equals quality. More rounds, harder coding challenges, longer interview loops, and higher rejection rates are often viewed as evidence of a strong hiring process. But as we explored during the webinar, many organizations are confusing difficulty with effectiveness. A process can feel rigorous and still be fundamentally misaligned with what the role actually requires.
So if you watched the webinar, what’s next? The answer isn’t rebuilding your hiring process from scratch. It’s taking a hard look at whether your process is measuring engineering capability or measuring something else entirely.
Why do rigorous technical interviews fail to identify the best engineers?
One of the most important takeaways from the discussion is that what might be seen as rigor in your process could be unnecessary friction. Many technical interviews require candidates to code while being observed, explain every decision in real time, manage social interactions, handle interruptions, and solve complex problems under pressure. The assumption is that all of these activities provide useful hiring signals.
But do they?
Most engineers don’t spend their days coding in front of an audience while narrating every thought process. They spend their time researching, debugging, testing, iterating, and solving problems independently before collaborating with teammates. When interview environments look dramatically different from actual work environments, hiring teams risk measuring interview performance instead of job performance.
This is one of the first areas where CoderPad can help. Instead of relying solely on high pressure live exercises, teams can build structured assessments that reflect the work engineers actually do. The result is a clearer signal and a more accurate evaluation of technical capability.
Why are top engineering candidates dropping out of your hiring funnel?
Many organizations proudly point to low pass rates or high candidate drop off as proof that their standards are working. The reality is often more complicated.
Candidates don’t only leave because they aren’t qualified. They leave because they become disengaged. They leave because the process feels unnecessarily difficult. They leave because they don’t see a connection between the assessment and the role they’re applying for. And increasingly, top candidates leave because they have other options.
The best engineers are often evaluating your company just as closely as you’re evaluating them.
When candidates encounter unclear expectations, inconsistent interviews, or assessments that feel disconnected from real work, they’re learning something about your organization. Unfortunately, that lesson may not be the one you intended to teach.
CoderPad helps reduce that friction by creating a consistent experience across candidates. Everyone receives the same challenge, the same expectations, and the same evaluation criteria, making the process more transparent for candidates and more defensible for hiring teams.
Should candidates be allowed to use AI during technical interviews?
No topic generated more discussion during the webinar than AI.
Today’s engineering teams are increasingly expected to use AI as part of their daily workflow. AI assisted coding, debugging, documentation, and problem solving are quickly becoming standard practice across the industry. Yet many hiring processes still evaluate candidates as if those tools don’t exist.
That’s creating a growing disconnect.
The question is no longer whether engineers should use AI. The question is whether your hiring process reflects how engineering work actually gets done today.
What is AI fluency in technical hiring?
AI fluency is a candidate’s demonstrated ability to work effectively alongside AI tools in realistic, role-relevant engineering scenarios. It includes prompting, debugging, reviewing AI-generated code, and integrating AI outputs into a working solution.
AI fluency is distinct from AI presence (listing AI tools on a resume) or AI familiarity (having used AI tools casually). It is a measurable, assessable skill — and one that traditional interviews cannot evaluate.
Hiring teams that assess for AI fluency now are building engineering organizations that will outperform those still hiring on credentials alone.
How should hiring teams design better technical assessments?
Three principles guide smarter technical hiring design:
1. Mirror real work environments Replace or supplement live coding exercises with structured technical assessments that reflect how engineers actually solve problems on the job. Candidates perform in conditions closer to real work; hiring teams get a more accurate signal.
2. Standardize evaluation criteria Inconsistent interviews introduce bias and reduce signal quality. Role-relevant exercises with defined rubrics give every candidate the same opportunity to demonstrate capability — regardless of communication style, neurotype, or educational background.
3. Design for the work, not the performance Ask: if our best engineer applied today, would our interview process help them succeed — or accidentally screen them out? Every assessment element should map to a skill the role actually requires.
What is a technical hiring audit?
A technical hiring audit is a structured review of your interview process, assessment strategy, candidate experience, and evaluation framework — designed to identify hidden friction, bias, and signal loss that prevent qualified engineers from advancing.
The goal isn’t to make hiring easier. It’s to make sure your bar is measuring the right thing. Our team of experts are ready and available to help with these audits. You can request time with us here.
Frequently Asked Questions
What’s the difference between interview performance and job performance? Interview performance measures how well a candidate handles the specific conditions of an interview — observation, time pressure, social evaluation. Job performance measures how effectively they build, debug, collaborate, and deliver in their actual work environment. When interview conditions differ significantly from real work conditions, the two can diverge substantially.
What is structured technical assessment? A structured technical assessment is a standardized, role-relevant evaluation where every candidate receives the same challenge, instructions, and scoring criteria. Structured assessments reduce interviewer subjectivity, improve consistency, and produce stronger predictive validity for job performance than unstructured live interviews.
How does bias enter technical hiring? Bias enters through resume screening (school names, company logos), unstructured interview formats that reward certain communication styles, and live evaluations where social performance influences technical scores. Consistent rubrics and role-relevant assessments evaluated against objective criteria reduce bias by focusing evaluation on demonstrated capability.
What does CoderPad offer for technical hiring? CoderPad provides an assessment and interview platform that helps engineering hiring teams build structured assessments, standardize evaluation criteria, and create interview experiences that reflect real engineering workflows. Products include Qualify (automated pre-screening), AI Interview Designer (structured interview generation), Benchmark AI (data-driven candidate comparison), and AI Fluency assessments.
Want to find out whether your process is finding great engineers or filtering them out? Book a Technical Hiring Audit with CoderPad