We Need New Ways to Interview Engineers: The Old Methods Are Broken

Imagine you’re a skilled software engineer, but every time you go in for an interview, you’re asked to solve a problem you haven’t thought about since your university days. Maybe it’s an algorithm you could easily look up if needed at work. Or worse, you’re stuck whiteboarding a solution while three people silently judge your every move. Does that sound like an environment where you would normally work? Probably not.

The truth is, the way we interview engineers today is broken. The process is detached from the reality of modern engineering, often focusing on the wrong things. If you’re an engineer who spends your day building systems, collaborating with a team, debugging code, and writing production-ready software, you might be wondering: why aren’t interview processes built to test *that*? Why are interviews still stuck in the past, emphasizing tasks that don’t reflect the real-world demands of engineering?

The Problem with Today’s Engineering Interviews

Let’s start with the obvious. Most technical interviews focus on algorithmic puzzles, whiteboard coding sessions, and problems that require more memorization than skillful application of knowledge. These tasks rarely simulate the actual work environment engineers face. Day-to-day, engineers rely heavily on tools, collaboration, problem-solving skills, and a deep understanding of systems and architecture. Yet, many companies still stick to these outdated methods of vetting candidates, as though solving an algorithm without Google or Stack Overflow somehow guarantees future success.

Here’s why this approach is flawed:

1. Memorization over application:
A majority of engineering interviews test candidates on their ability to memorize algorithms and data structures rather than applying their skills to solve real-world problems. While a solid understanding of algorithms is important, many engineers don’t spend their workday doing this. They’re writing production-level code, debugging, and iterating based on feedback. Memorization tests are a poor indicator of someone’s ability to do this effectively.

2. The artificial environment:
Imagine being asked to code without access to your IDE, documentation, or Google. For most engineers, that’s not a realistic scenario. Engineers are problem solvers who use a variety of resources and tools to build effective solutions. Whiteboard interviews strip away these tools, reducing an engineer’s ability to demonstrate how they actually work.

3. Not representative of actual job tasks:
Engineers spend a lot of their time reading existing code, debugging, collaborating, and architecting systems. Rarely does a software engineer’s daily work involve writing quick solutions to isolated algorithmic problems. By focusing exclusively on algorithms and coding puzzles, interviews fail to measure the skills that matter most on the job: teamwork, design thinking, code quality, and iterative problem-solving.

What Engineers Do at Work is Different from What They’re Tested On

At work, engineers operate within complex environments. They collaborate with cross-functional teams, manage technical debt, handle edge cases, and deploy code into production. They also use a suite of tools—IDEs, version control systems, CI/CD pipelines, and now, AI-powered tools like GitHub Copilot or ChatGPT for code assistance.

The discrepancy between what engineers do on the job and what they are tested on during interviews is glaring. A typical coding interview doesn’t test if a candidate can work well with version control, debug a tricky issue in a system, or write scalable, maintainable code. And it certainly doesn’t assess how an engineer can leverage tools like AI to optimize their workflow, a skill that is increasingly essential.

The Rise of AI in Engineering: A New Norm Requires a New Process

We are now in the era of AI assistance for coding. Tools like GitHub Copilot and ChatGPT are rapidly becoming indispensable for engineers. These AI tools help with everything from writing boilerplate code to suggesting optimizations or even generating entire codebases. If we accept that AI-powered coding is part of the future of software development, shouldn’t we be evaluating engineers on how well they integrate these tools into their workflows?

Current interview processes don’t reflect the new norms of engineering. Instead of testing engineers on their ability to write code entirely from scratch, we should be evaluating their ability to solve problems by using the best tools available, including AI. In the same way that we no longer require engineers to write assembly code for basic tasks, we shouldn’t expect them to ignore the AI-powered tools that make them more efficient.

A Call for Change: What a Modern Engineering Interview Should Look Like

1. Simulate the actual work environment:
Interviews should mimic what engineers actually do on the job. This means having access to an IDE, version control, and documentation, and solving real-world problems in a collaborative setting. This could involve debugging real code, solving design challenges, or refactoring a messy codebase.

2. Evaluate how engineers use AI tools:
As AI becomes a standard part of the engineering toolkit, interviews should test how candidates use these tools. Are they efficient at leveraging AI for routine tasks? Can they use AI to brainstorm solutions or catch errors? These are the skills of the future, and interviews need to adapt accordingly.

3. Focus on problem-solving, not memorization:
Instead of asking engineers to memorize algorithms, focus on their ability to solve problems. Give them realistic tasks that mirror the challenges they’d face in the role, whether it’s debugging an issue, designing a scalable system, or refactoring old code.

4. Test collaboration and communication:
Engineering isn’t a solo sport. Most of the time, engineers work in teams, and their ability to communicate effectively is just as important as their technical skill. Interviews should assess how well candidates work with others, give and receive feedback, and explain complex technical ideas in simple terms.

Conclusion

The engineering interview process is overdue for an overhaul. As the nature of engineering work changes, particularly with the rise of AI tools, so too must the way we evaluate talent. Instead of relying on outdated methods that test memorization and artificial coding environments, we need new ways to interview engineers that reflect the realities of modern software development.