Google Revamps Software Engineer Interviews to Assess AI Fluency

Google is restructuring its software engineering interview process to reflect the growing use of artificial intelligence in everyday development work. Internal documents reviewed by Business Insider show that Google has been rolling out AI-related hiring pilots in phases this year, including the use of Gemini as an interview assistant for coding interviews. The changes build on a May report that the company was testing new interview formats within a small number of organizations, including a code comprehension assessment that evaluates candidates on AI fluency and prompt engineering. The updated formats have since been expanded more broadly across the company, although a spokesperson said they remain in pilot status. Brian Ong, Google’s vice president of recruiting, said the company is testing updates to its software engineering interviews, including evaluations of AI fluency, to better reflect how Google’s teams operate. For software engineering positions, AI fluency is now a requirement, while AI product integration is required for most roles outside entry-level positions. Google is also using Gemini in other parts of the interview process. The company is piloting Gemini-powered transcription and note-taking for interviews conducted on Google Meet, and is working to let interviewers use Gemini to draft role-tailored candidate feedback. The goal is to streamline evaluation processes as AI becomes more central to software development. The changes follow Google’s disclosure in April that three-quarters of new code created at the company was generated by AI and reviewed by humans, a trend that has become common across the technology industry. As more developers rely on AI coding assistants, companies are seeking to assess not only traditional programming skills, but also how effectively candidates can work with AI tools. Google has cautioned employees not to develop independent AI systems for hiring or interviews. Internal guidance warned staff against using generative AI or large language models to create their own standalone solutions or agents for recruiting use cases, indicating that the company wants centralized oversight as it experiments with AI in its hiring pipeline.