How smart teams should update technical interviews for software recruiting in the AI era

Artificial Intelligence is rapidly changing how software engineers work, yet many companies continue to rely on traditional technical interview processes that may no longer effectively evaluate modern engineering skills.

Current interview landscape: Technical interviews at major tech companies typically involve coding challenges on platforms like LeetCode and system design exercises that follow predictable patterns.

• Engineers often prepare by studying resources like “Cracking the Coding Interview” and practicing algorithmic puzzles that bear little resemblance to day-to-day engineering work
• System design interviews have become formulaic, following a standard 10-step process that rarely explores true architectural complexity
• These traditional approaches persist despite AI’s growing capability to solve coding challenges and generate functional code

AI’s impact on technical assessments: Large Language Models (LLMs) are now capable of solving complex coding challenges, calling into question the effectiveness of traditional technical interviews.

• One senior hiring manager found that LLMs could now successfully complete their second-round technical assessments in a single attempt
• AI tools have demonstrated the ability to solve algorithmic puzzles hundreds of times faster than human engineers
• The rapid advancement of AI in coding tasks suggests a need to reconsider how technical skills are evaluated during interviews

The case for code reviews: Code review exercises may offer a more practical assessment of engineering capabilities in an AI-powered development environment.

• Code reviews naturally evaluate communication and collaboration skills, which are difficult to assess during traditional coding challenges
• Review exercises can test both breadth and depth of knowledge across the full technology stack
• Candidates can demonstrate practical skills like identifying bugs, suggesting optimizations, and evaluating security concerns

Alternative assessment strategies: A more comprehensive technical interview process could incorporate multiple evaluation methods.

• Reviews of small React applications, APIs, and database schemas can reveal a candidate’s strengths across different technical domains
• Deliberately introduced bugs and performance issues can test a candidate’s ability to identify and solve real-world problems
• Database design reviews can assess understanding of data types, indexing strategies, and query optimization

Looking ahead: In an era where AI increasingly handles code generation, the ability to evaluate and improve code quality becomes more crucial than traditional coding skills.

• Teams should consider adapting their technical screening processes to reflect the growing role of AI in software development
• The distinction between writing and evaluating code may become less important as AI tools become more sophisticated
• Engineering teams may need to prioritize candidates who excel at shepherding and improving AI-generated code rather than those who simply solve coding puzzles quickly

Evolving skill requirements: As the software engineering field continues to transform, companies must reassess which technical capabilities truly matter for modern development teams.

• The ability to effectively evaluate code quality, security, and performance may become more valuable than raw coding skills
• Teams should begin incorporating code review exercises into their interview processes alongside traditional assessments
• Future technical interviews may need to focus more on how candidates can work with and improve AI-generated code rather than their ability to solve algorithmic puzzles