Artificial Intelligence
Consulting
Development ServicesAn engineering manager at a growing SaaS company recently shared a story with our team. A take-home assignment that once took strong candidates about three hours came back in under an hour. The solution was clean, well-structured, and handled edge cases effectively. On paper, everything looked excellent.
But when the candidate was asked a few key questions in the follow-up discussion, he stumbled and paused. The answers were technically accurate but shallow, as if reviewing someone else's reasoning. The code was solid, but the engineer struggled to fully own and explain the tradeoffs.
This scenario is increasingly common as AI transforms how engineers work.
Why Traditional Technical Interviews No Longer Work
Traditional take-home assignments and whiteboard interviews were designed to test how quickly candidates could write code and solve problems on their own.
In 2026, most developers use AI assistants for a large portion of their daily work. That means writing code quickly is no longer the skill that sets the best engineers apart. What matters more is judgment: knowing when to trust AI-generated code, when to improve it, how to evaluate tradeoffs, and how to explain technical decisions to the teams that will maintain the software over time.
A 2026 survey of 400 engineering leaders across the US, India, and China found that 71% say AI has made it harder to assess candidates’ true technical skills.
Traditional formats simply don’t measure what matters most today.
How Top Companies Are Adapting Their Hiring Process
Here are a few examples of how brands and organizations have adapted their interviews to reflect what’s important in the tech landscape today.
Canva expects backend, frontend, and machine learning candidates to use AI tools (Copilot, Cursor, Claude) during interviews. Tech and HR leaders assess prompting, code review, debugging, and explanation skills, not just the final output.
Shopify uses AI-enabled coding rounds. Candidates bring their own IDE and build features from scratch, such as a background job processor or shopping cart system. They can use tools like Cursor or GitHub Copilot for boilerplate, but hiring managers watch closely how candidates drive the architecture, explain their choices, incorporate feedback, and verify the AI’s work. The focus is on practical decision-making and collaboration.
Meta added an AI-enabled coding round in late 2025, now expanding in 2026. It replaces one traditional coding session in the onsite loop. Candidates work in a CoderPad setup with a built-in AI assistant (including options like Claude, GPT, or Gemini). In this session, they tackle multi-file problems - fixing bugs, implementing features, and optimizing - while evaluators assess how well they prompt the AI, review its output, debug issues, and take ownership of the final solution. They still keep one no-AI coding round for balance.
Many tech teams in other leading brands and organizations also prioritize live discussions around work samples. These sessions reveal how candidates defend decisions, explore alternatives, and adapt under constraints - skills difficult to fake or fully outsource to AI in real time.
Evaluating Engineering Partners and Nearshore Teams in the AI Era
Hiring full-time engineers isn't the only place this matters. The same challenges apply when evaluating staff augmentation providers, nearshore teams, or development partners that incorporate AI into their workflows. The key question is whether their teams can truly understand, explain, and take ownership of the code they deliver.
Practical evaluation that works:
- Work sample reviews + deep follow-up conversations. Ask targeted questions: What would you change if data volume increased 10x? Where were you uncertain during development, and how did you resolve it?
- Reference checks focused on judgment: Specific examples of pushing back on risky AI suggestions, catching issues early, or guiding output toward maintainable, secure solutions.
- Short, structured technical discussions early in engagements, based on real or near-real work. This quickly distinguishes AI-generated output from engineers who can steward it responsibly.
What Makes a Great Engineer
The interview process isn't the only thing that needs to change: our definition of a great engineer is evolving too.
AI tools can help developers work faster, but they also introduce new risks. ACM’s April 2026 TechBrief on “vibe coding” highlights increased security vulnerabilities, technical debt, and maintenance challenges in heavily AI-assisted codebases. As AI-generated code becomes more common, the ability to review, question, and improve that code becomes increasingly important.
Today, the best engineers aren't defined by how much code they can write on their own. They're the ones who understand the tradeoffs, catch mistakes, and can explain why a solution is the right one. As AI becomes part of everyday development, those skills matter more than ever.
Actionable Takeaways for Tech Leaders
- Update requirements to explicitly include AI fluency (prompting, verification, debugging).
- Shift emphasis toward ownership and judgment in interviews and partner evaluations.
- Combine work samples with live discussions for clearer signals.
- For nearshore or augmentation partners, test real collaboration early.
At Intersog, our nearshore teams in Canada, Mexico, and Israel combine strong technical delivery with the critical thinking and ownership essential in the AI era. We help companies scale reliably while maintaining quality and security.
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FAQ
How has AI changed technical interviews in 2026?
AI tools make execution easier, so interviews now focus more on judgment, tool fluency, and ownership. 71% of engineering leaders report greater difficulty assessing skills.
Which companies allow AI in interviews?
Canva, Meta, Shopify, Rippling, and Google are leading the way by evaluating how candidates collaborate with AI.
What should I look for when hiring nearshore or augmented teams?
Prioritize partners whose engineers can explain design decisions, defend tradeoffs, and manage AI output responsibly, beyond just clean code.
This post is based on 2026 industry surveys and practices from leading tech companies. Last updated: June 2026.
