Make talent quality your leading analytic with skills-based hiring solution.
Engineering roles stay open longer than almost any other position. You know the pattern: a req opens, applications flood in, resumes get reviewed, phone screens happen, and then the pipeline stalls. Candidates who looked strong on paper can’t solve basic problems. Others talk a good game but can’t write functional code. By the time you realize someone isn’t qualified, you’ve burned weeks of calendar time and lost credibility with the hiring manager.
The issue isn’t volume. Most companies get plenty of applicants for engineering roles. The problem is separating people who can do the work from people who just know how to describe it. That’s where a well-designed engineer test and technical assessment become critical tools for TA teams.
Resumes have always been an imperfect screening tool, but the gap has widened. Candidates know which keywords to include. They list frameworks, languages, and methodologies that sound impressive. Some have worked at recognizable companies. But none of that tells you whether they can debug a production issue, design a scalable system, or write code that other engineers can maintain.
Phone screens help filter some candidates, but they’re inconsistent. One interviewer focuses on algorithms. Another asks about system design. A third spends most of the call talking about company culture. When every candidate gets a different conversation, you can’t meaningfully compare their abilities. And the engineers conducting these screens have their own work to do. Pulling them away for hours of calls every week creates its own problems.
Without a reliable way to measure technical ability early, you end up with two bad outcomes. Either you advance too many unqualified candidates and waste everyone’s time in later interviews, or you screen too aggressively and lose good people who didn’t happen to shine in an unstructured phone call. Neither option is sustainable when you’re trying to fill multiple engineering roles quickly.
An engineer test exists to answer one question: can this person do the job you’re hiring them for? That seems simple, but many assessments miss the point. They test memorization instead of application. They focus on edge cases that rarely come up in real work. They penalize candidates for not knowing a specific syntax while ignoring whether they can solve actual problems.
The best technical assessments measure skills that predict job performance. If your backend engineers spend time optimizing queries and building APIs, the test should evaluate those abilities. If your frontend team needs people who can build responsive interfaces and manage state effectively, those should be the focus areas. The assessment should feel relevant to candidates and produce results that hiring managers trust.
This is where most off-the-shelf testing platforms fall short. They offer generic question libraries that aren’t tailored to your specific needs. A Java developer and a Python developer might take essentially the same test, even though the roles require different strengths. That lack of specificity creates noise in your results and makes it harder to identify the right candidates.
Glider AI takes a different approach to technical assessment. Instead of generic tests, the platform lets you evaluate candidates using scenarios and challenges that mirror your actual work environment. Here’s what that looks like in practice.
Their assessments don’t rely on trivia or syntax memorization. Questions are designed to test how candidates approach problems similar to what they’ll encounter in the role. For a DevOps engineer, that might mean troubleshooting a deployment pipeline issue or optimizing infrastructure costs. For a data engineer, it could involve designing an ETL process or debugging a query performance problem.
This relevance matters because it gives you signal instead of noise. A candidate who can solve realistic problems is more likely to succeed in the role than someone who just memorized algorithm patterns for interviews. And when candidates see that the assessment reflects actual work, they take it more seriously. You get a better sense of their true abilities.
Writing code in isolation is easier than writing code that integrates with existing systems. The platform includes simulation environments where candidates work on problems that require more than just syntactically correct code. They might need to refactor an existing codebase, integrate with an API that has specific constraints, or debug an issue using realistic logs and error messages.
These simulations reveal important qualities that traditional tests miss. Does the candidate read documentation carefully, or do they make assumptions? Do they test edge cases, or do they only handle the happy path? Do they write code that’s maintainable, or is it a brittle mess that works once? These behaviors predict job performance better than whether someone can implement a red-black tree from memory.
One of the biggest challenges in technical hiring is maintaining consistency across candidates. Different reviewers have different standards. What one person considers acceptable code, another might reject. This inconsistency makes it hard to compare candidates fairly and often leads to arguments between TA teams and hiring managers.
They addresses this with structured scoring tied to specific competencies. When a candidate completes an engineer test, the platform evaluates their response against clear criteria. You see exactly where they performed well and where they struggled. The scoring isn’t just a pass or fail grade. It’s a breakdown of strengths and gaps that helps you make informed decisions.
This standardization also makes the hiring process more defensible. When a hiring manager questions why someone didn’t advance, you can point to specific assessment results rather than subjective impressions. And when you do advance candidates, you can explain exactly what skills they demonstrated.
Remote technical assessments create opportunities for cheating. Candidates might get help from others, copy solutions from the internet, or use tools inappropriately. If you can’t trust the results, the entire assessment becomes worthless.
Glider AI includes proctoring and integrity monitoring to address this risk. The platform tracks behavior during the test, flags suspicious activity, and uses techniques like question randomization to prevent answer sharing. This doesn’t create a hostile testing environment. It simply ensures that the results you see actually reflect each candidate’s abilities.
When you know the assessment results are valid, you make better hiring decisions. You don’t waste time interviewing someone who only passed because they had outside help. And you don’t unfairly eliminate candidates whose honest results got lost in noise from others gaming the system.
Most assessment tools give you candidate scores and nothing else. Glider provides analytics that help you understand your entire hiring process. You can see which technical assessment questions best predict success, where candidates commonly struggle, and how your results compare to industry benchmarks.
This feedback is invaluable for continuous improvement. If you notice that strong candidates consistently struggle with a particular question, maybe that question doesn’t measure what you think it does. If certain skills gaps appear repeatedly, that might indicate a problem with your sourcing strategy or job description. The analytics turn assessment data into actionable insights about how to hire better.
Time is the hidden cost in technical hiring. TA teams spend hours reviewing resumes, scheduling calls, and coordinating interviews, only to discover that many candidates can’t do basic work. Those hours add up quickly when you’re hiring multiple engineers.
A well-implemented engineer test changes this dynamic. Instead of manually screening everyone, you use Glider AI’s technical assessment to filter candidates based on demonstrated ability. The platform handles the evaluation work, and you focus your time on candidates who’ve already proven they have the necessary skills.
This doesn’t mean you stop interviewing people. It means the interviews become more productive. You’re not trying to figure out if someone can code. You already know they can. The interview is about assessing fit, communication, problem-solving approach, and how they’d work with your team. Those conversations are more valuable and less likely to reveal disqualifying gaps.
The time savings compound across multiple roles. If you’re hiring five engineers and each req requires screening fifty candidates, that’s 250 assessments. Doing that manually would take hundreds of hours. With Glider, the platform handles the technical evaluation, and your team focuses on the candidates worth your attention.
Bias affects technical hiring in ways that aren’t always obvious. Resumes from candidates with certain university backgrounds get more attention. Candidates who’ve worked at well-known companies get the benefit of the doubt. Even something as arbitrary as name recognition can influence whether someone gets a fair look.
Glider AI’s technical assessment platform reduces these biases by focusing on what people can do rather than where they’ve been. Every candidate takes the same engineer test. Every response is scored using the same criteria. The platform doesn’t care about pedigree or credentials. It only measures ability.
This creates opportunities for candidates who might otherwise get screened out. Someone who learned to code through a bootcamp or self-study gets the same chance to demonstrate their skills as someone with a computer science degree from a top university. And TA teams get access to a more diverse pipeline without lowering their quality bar.
A technical assessment is only useful if it predicts whether someone will succeed in the role. Glider AI’s platform is built around this principle. The skills being tested are chosen based on what actually matters for job performance, not what’s traditionally included in coding interviews.
Over time, you can validate this connection by tracking how assessment scores correlate with employee outcomes. Do people who score well in specific areas tend to get strong performance reviews? Do certain assessment results predict longer tenure or faster ramp-up time? This data helps you refine your hiring criteria and gives you confidence that the engineer test is measuring the right things.
When hiring managers see that candidates who pass Glider AI assessments consistently perform well, they trust the process. That trust makes your job easier. You spend less time defending your candidate choices and more time filling roles with people who contribute from day one.
Engineering hiring will always require significant effort. The roles are complex, the talent market is competitive, and the cost of mistakes is high. But the right tools make the process more manageable and more effective.
Glider AI’s approach to technical assessment gives TA leaders what they need: reliable data about candidate abilities, reduced time spent on manual screening, standardized evaluation that supports fair comparisons, and insights that help improve the hiring process over time. An engineer test isn’t a replacement for good interviewing or thoughtful decision-making. It’s a tool that makes those activities more focused and more likely to produce good outcomes.
When you implement a technical assessment strategy that actually measures job-relevant skills, several things happen. Your time to fill drops because you’re not wasting weeks on unqualified candidates. Your quality of hire improves because you’re making decisions based on demonstrated ability rather than resume keywords. And your hiring managers develop confidence in the TA team’s ability to send them people who can do the work.
For HR and TA leaders managing technical hiring, the question isn’t whether to use assessments. It’s whether your current approach gives you the clarity and efficiency you need. Glider AI’s platform is designed to deliver both, helping you make confident hiring decisions even in competitive markets and high-volume scenarios.
Glider AI uses AI Proctoring technology to monitor test-taking behavior, randomizes questions to prevent answer sharing, and tracks how candidates write code, not just final answers. This multi-layered approach flags suspicious activity and ensures results reflect actual abilities.
Track whether candidates who score well also perform well on the job. Glider AI’s analytics show which assessment areas correlate with strong performance reviews and faster ramp-up times. Regular feedback from hiring managers also confirms whether passing candidates have the skills teams actually need.
Time to fill typically decreases. While assessments add an initial step, you waste less time interviewing unqualified candidates. The people who reach interviews are more likely to succeed, leading to faster hiring decisions and fewer failed searches.
Use judgment. If the assessment measures job-critical skills, interview performance may reflect communication ability rather than technical competency. For borderline cases, consider a take-home project or additional technical conversation to gather more data before deciding.
Review assessments quarterly and update when your technology stack changes or when analytics show certain questions don’t predict performance well. You don’t need constant changes, but keep content aligned with current role requirements and your actual work environment.
Test Technical Skills, Not Assumptions Hiring technical talent is one of the toughest challenges recruiters face today. Resumes often fail to reflect real expertise, and interviews can be biased or inconsistent. Report by Upturn highlights that employers often face challenges in assessing candidates’ technical skills, leading to potential mis-hires and resource wastage. That’s where a […]
