How to Assess Technical Skills Effectively Without Being a Subject Matter Expert

The recruiter had been at it for three years. She'd hired designers, product managers, marketers, and salespeople. She knew how to spot talent. She knew how to probe for depth. She knew what good looked like in every function she'd hired for.

Except engineering.

When she screened engineers, the conversation felt different. They'd talk about frameworks she'd never heard of, architectural decisions she couldn't evaluate, and problems she didn't have context for. She could assess whether they communicated well. She could tell if they seemed thoughtful or engaged. But she couldn't tell if they actually knew what they were doing.

So she'd ask the standard questions. "Tell me about a technical challenge you've faced." "Walk me through a project you're proud of." The candidate would answer, and she'd take notes, nodding along. Then she'd pass them to the engineering team for a technical screen, hoping she'd gotten it right.

Sometimes she had. Sometimes she hadn't. And the uncertainty gnawed at her. She was supposed to be the filter, the person who saved the engineering team's time by only moving forward candidates who were ready. But without technical expertise herself, she felt like she was guessing.

This is the recruiter's dilemma. You're expected to screen for skills you don't have. And in tech hiring, where the gap between what a resume says and what a candidate can actually do is often enormous, this creates a structural problem that most companies haven't figured out how to solve.

The knowledge gap is real

You can train a recruiter to understand the basics. What a backend engineer does. What languages are commonly used for what purposes. What makes a senior engineer different from a junior one. This is useful. It helps with sourcing. It helps with initial conversations.

But it doesn't solve the core problem. Knowing what Python is used for doesn't tell you whether a candidate actually knows Python. Knowing that system design is important for senior roles doesn't tell you whether a candidate can design a system. The terminology helps you ask the right questions, but it doesn't help you evaluate the answers.

A candidate can say they've worked with microservices. They can describe a project where they built an API. They can use words like "scalability" and "latency" and "distributed systems." And to someone without technical background, it all sounds plausible. But an engineer listening to the same answers might immediately spot gaps. Shallow understanding. Buzzword fluency without substance.

The recruiter doesn't have that filter. They're relying on trust. Trust that the candidate is accurately representing their experience. Trust that the way the candidate talks about their work reflects the quality of the work itself. And that trust gets violated constantly, not because candidates are dishonest, but because people naturally overestimate their competence and underestimate how much they don't know.

The proxy trap

Without the ability to assess technical depth directly, recruiters rely on proxies. Where did the candidate go to school? What companies have they worked at? How long have they been in the field? Do they speak confidently about their experience?

These proxies aren't useless. Someone with a CS degree from Stanford and three years at Google is more likely to be strong than someone who's self-taught and has bounced between small startups. But "more likely" isn't the same as "definitely," and relying too heavily on proxies means you miss people.

The self-taught developer who's been contributing to open source for five years might be better than the Stanford grad. The candidate who worked at a small company might have had more ownership and learned faster than the one who worked at a big company on a narrow piece of a system. The person who speaks haltingly in an interview might be a careful thinker who's better at code than conversation.

Proxies create false confidence. They let you make decisions quickly, but they don't make those decisions more accurate. And in a market where talent is scarce and competition is high, missing strong candidates because they don't have the right proxies is a costly mistake.

The technical screen becomes a crutch

Because recruiters can't assess technical skill themselves, they lean heavily on the engineering team. Every candidate who passes the recruiter screen gets handed off for a technical evaluation. This seems logical. Let the experts handle the expert assessment.

But it creates a different problem. If the recruiter screen isn't filtering effectively, the technical screen becomes overloaded. Engineers spend hours talking to candidates who aren't ready. They get frustrated. They start to wonder why the recruiter is moving people forward who clearly don't meet the bar.

The recruiter, meanwhile, is stuck. If they're too conservative, they'll miss people. If they're too liberal, they'll waste engineering time. And because they can't assess technical depth themselves, they don't have a good way to calibrate. They're guessing, and the feedback loop is slow and unclear.

The system becomes inefficient. The recruiter does a lot of screens that don't lead anywhere. The engineer does a lot of screens that shouldn't have happened. The candidate gets a poor experience because they're advancing through stages they're not ready for, only to get rejected later. Everyone loses time, and no one is happy with the process.

What you can assess without being technical

Here's the thing: recruiters can't evaluate technical skill directly, but they can evaluate other things that matter. And some of those things correlate with technical competence.

Problem-solving ability. Can the candidate break down a complex question into components? Do they think systematically, or do they jump to conclusions? When you ask them to explain something, do they organize their thoughts clearly, or do they ramble?

Learning orientation. Do they talk about what they've learned recently? Do they mention gaps in their knowledge without defensiveness? Do they seem curious about new tools, or are they stuck in what they already know?

Ownership. Do they describe their work in terms of problems solved, or just tasks completed? Do they take responsibility for outcomes, or do they blame external factors when things didn't go well?

These aren't technical skills, but they're predictive of technical performance. A candidate who can't explain their thought process clearly probably isn't a strong problem-solver. A candidate who hasn't learned anything new in two years probably isn't keeping up with a fast-moving field. A candidate who talks about their work in vague, passive terms probably didn't have much ownership over it.

Recruiters can assess these things. They're not a substitute for technical evaluation, but they're useful signals. And they help narrow the pool before engineering time gets involved.

Structure compensates for expertise

The real solution, though, isn't to train recruiters to be pseudo-engineers. It's to build structure into the process that doesn't depend on individual expertise.

Structured interviews work because they standardize evaluation. Every candidate gets asked the same questions. Every answer is scored against the same rubric. The person conducting the interview doesn't need deep expertise, because they're not making judgment calls. They're following a framework.

This works for behavioral interviews. It's harder for technical ones, because technical questions require technical understanding to evaluate. You can ask a candidate to explain how they'd optimize a slow database query, but if you don't know anything about databases, you can't tell if the answer is good.

This is where AI-led technical interviews change the dynamic. When a candidate completes a structured screen through something like Expert Screen, the AI asks technical questions, listens to the answers, and evaluates them against a rubric tied to the skills the role requires. It can assess coding fundamentals, data structures, problem-solving, and system design. It can probe deeper when an answer is vague. It can adjust difficulty based on the candidate's level.

The recruiter doesn't need to be technical. They just need to review the results. The AI generates a scorecard, a transcript, and a video recording. The recruiter sees how the candidate performed on specific technical areas. They see whether the candidate met the bar. And they make a decision about whether to move them forward based on structured, comparable data.

This doesn't eliminate the need for engineering involvement. The hiring manager still does a final technical interview. But by the time a candidate reaches that stage, they've already demonstrated baseline competence. The engineering interview becomes about depth and fit, not about whether the person knows the basics.

Trust, but verify

Here's another angle: resume verification. A candidate lists five years of experience with React. They say they've built production applications. They describe projects that sound impressive. But is any of it true?

Recruiters take resumes at face value because they don't have a way to verify them. You can ask the candidate to walk through their experience, but if they're exaggerating or misrepresenting, you might not catch it. The mismatch only surfaces later, in the technical interview or worse, after they're hired.

This isn't about catching liars. Most candidates aren't lying. But they are overstating. They list skills they've only dabbled in. They describe roles with more responsibility than they actually had. They take credit for team efforts as if they were individual accomplishments. These distortions are normal, but they're costly if they're not caught early.

Resume validation, done with structure and rigor, gives recruiters a tool they don't currently have: a way to verify technical claims without being technical themselves.

Clarity improves decisions

When recruiters have better information, they make better decisions. Not perfect decisions, but better ones. They move forward candidates who are actually ready. They filter out candidates who aren't. They save engineering time. They reduce time-to-hire. And they stop feeling like they're guessing.

This doesn't mean recruiters become technical experts. They don't need to. They just need tools that give them the information they need to do their job well. Tools that assess what they can't assess. Tools that structure what's currently unstructured. Tools that bring rigor to the part of the process that's currently the most subjective.

The goal isn't to replace human judgment. It's to support it. The recruiter still decides who moves forward. The hiring manager still makes the final call. But they're working with better inputs. And better inputs lead to better outcomes.

The shift is already underway

The companies figuring this out aren't asking their recruiters to become engineers. They're building systems that let recruiters do what they're good at, using tools that handle the parts they're not equipped for.

Recruiters assess communication, motivation, and culture fit. AI assesses technical depth, problem-solving, and skill verification. The engineer steps in later, for candidates who've already cleared a meaningful technical bar. Everyone operates in their zone of competence. No one is guessing.

This is how you assess technical skills without being a subject matter expert. Not by faking expertise you don't have. But by using structure, tools, and process to capture the signal you need, even when you can't evaluate it directly yourself.

The recruiter's job is hard enough. It shouldn't also require them to be something they're not.