Study to Pass, Not to Build

As a full-stack developer with over a 10+ years of experience, I've had a lot of tech interview, both from a candidate’s perspective and as somebody who is responsible for bringing in new talent. Over the years, this has ranged from casual chats relating from problem-solving to extremely structured technical assessments. One style that stands out, and for all the wrong reasons, is the now somewhat infamous LeetCode-style interview. Though it may seem very effective at filtering out candidates quickly, but I’ve experienced that it very often fails to pick out those attributes that truly make somebody a good software engineer.

From what I have seen, LeetCode interviews tend to emphasize algorithmic puzzles that rarely reflect the real work that developers do. They’re an artificial measure of skills that might help you filter out candidates quickly, but often miss out on the very people who would be strong additions to a team. And, what’s worse, such interviews tell you next to nothing about a person’s ability to build, maintain, and ship software products to production.

The Change with AI

And it becomes even more of a problem with the rise of AI tools like ChatGPT and GitHub Copilot. They can solve algorithmic problems for you in seconds. So what are these interviews really testing? With AI assistance, candidates can now easily breeze through those very problems that were designed to identify and benchmark their raw problem-solving abilities. This is reducing the validity of the assessment itself.

Limited Real-World Use Cases

This brings up a important question: How much of your daily work as a software engineer is actually covered by LeetCode challenges? If you are working on building scalable web applications, large codebases, or focusing on user experience, the chances are that you won’t need to implement a sorting algorithm from scratch or find the shortest path in a binary tree. You're likely working with design systems, collaborating with other teams, and making new features.

Passion and Willingness to Learn More

In my opinion LeetCode-style interviews are missing something important: the passion and eagerness to learn. Anyone can memorize the solutions to algorithm challenge, but those driven by a real curiosity or passion for solving actual problems, will likely be more successful than others. It’s easier to teach someone who is passionate and motivated than someone who simply knows how to pass coding tests. Passionate engineers continuously learn, adapt, and grow, and they tend to be the ones who thrive in fast-changing environments.

Building Systems Professionally

Lastly, LeetCode problems tell you nothing about a candidate’s ability to build professional software systems. Writing scalable, maintainable, and clean code is very different from solving isolated puzzles. As an engineer, you're not hired to work on algorithmic puzzles, you're hired to ship reliable, well-architected features that meet user needs. LeetCode interviews simply don’t get to that point.

Why Companies Use LeetCode-Style Interviews

So why do so many companies stick with this flawed interview model? There are two main reasons:

1. Big tech companies have a high volume of applicants. With thousands of candidates for each position, they need a way to filter through the majority quickly. LeetCode challenges offer a fast, easy way to reduce the applicant pool to a more manageable size. Even if they reject some qualified candidates, they’ll still end up with enough to fill their roles.

2. Other companies blindly follow big tech’s lead. Smaller companies often imitate what big tech does without fully understanding why. They use LeetCode-style interviews even though they don’t face the same volume of applicants, which results in filtering out many qualified candidates and struggling to fill positions with strong engineers.

Conclusion

LeetCode-style interviews are outdated, especially with the current grow of AI, where tools can resolve problems faster. They don’t measure people's passion, real-world skills, or growth within a team. Instead of focusing on algorithmic puzzles, companies should shift towards assessing a candidate's ability to build software, their enthusiasm for learning, and their approach to solving practical problems.