A year ago, the debate over whether candidates should be allowed to use AI tools during take-home assignments was all the rage. Now, most engineering teams have moved on. The question isn't whether you use AI, but whether you can use it well. That's a big difference.
The shift has had a profound impact on technical hiring. Some candidates are getting a leg up, while others are struggling to keep up. To succeed, you need to understand what's going on. "Vibe coding" - directing AI tools with natural language to produce working code, then refining and integrating the output - is no longer a novelty, but a baseline expectation at many companies. Senior engineers now evaluate candidates on more than just their ability to write code from scratch. They want to see if you can write precise prompts, critically review AI-generated code, and know when not to use AI.
For instance, when I was interviewing for a role at a mid-sized startup, I was asked to write a prompt that would generate a specific function using AI tools. The interviewer didn't just want to see the output, but also how I arrived at the prompt and what I would do if the output wasn't what I expected. It's not just about using AI, but about using it intelligently.
The underlying skills are still the same - you need to understand data structures, system design, and code quality. But now, you also need to demonstrate that you can use AI tools as a force multiplier, not a crutch. Take-home projects are evolving to reflect this new reality. Many companies expect candidates to use AI tools and then explain how they used them, what they changed, and what they rejected. A well-structured submission shows where AI was used, what the candidate changed, and how they handled edge cases the AI missed.
Some companies are even conducting live coding sessions with AI assistance. This is not just about typing code fast, but about showing judgment - do you verify the output, adapt it intelligently, and explain the code you've accepted? It's a tough test, but it's also a great way to showcase your skills. I've seen candidates who can use AI tools effectively, but struggle to explain their thought process. That's a red flag.
So, what are companies looking for? Judgment over output. Can you evaluate code quality critically? Every engineer has access to the same generation tools, but what varies is the quality of judgment applied to what those tools produce. You also need to demonstrate system thinking - AI is good at generating functions, but it's much weaker at designing systems. Candidates who can describe architecture decisions clearly, including the tradeoffs and constraints, are rare and valuable.
Debugging real-world problems is also key. Finding a bug in unfamiliar AI-generated code under time pressure is a genuine test of whether you understand what the code is doing, not just what it's supposed to do. And let's not forget communication - the ability to explain complex technical decisions to a hiring panel who may include non-engineers is increasingly important. That hasn't been automated away.
To prepare for this new landscape, you should build a portfolio with AI context. For projects you've built with AI assistance, write up a brief technical note explaining what the AI generated, what you changed, and why. Practice AI-assisted problem solving deliberately - use tools like Cursor, GitHub Copilot, or Claude actively in your practice sessions. Notice where the suggestions are wrong, incomplete, or introduce subtle bugs. Know your fundamentals cold, and prepare for system design without AI. System design interviews have become more important, not less, because this is where AI tools are genuinely weak. Practice designing systems end-to-end - data flow, storage choices, API design, scalability considerations, failure modes.
It's not going to be easy, but if you can master these skills, you'll be in high demand. Xeito can help you find roles that match how you actually work - they match you with companies based on your actual tech stack and working style, not just job titles. Give it a try.
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