The Dawn of AI-Coding: Rethinking the Engineering Interview
Just like the advent of word processors transformed the nature of writing — making it more efficient and accessible without eliminating the need for skilled writers — AI code completion tools like GPT-4 and GitHub Copilot are reshaping the software engineering role.
As AI takes on an increasingly significant role in writing code, the focus for software engineers shifts from writing code to figuring out what code to write, guiding their AI tools, refining their output, and making architectural decisions. By embracing this change and adapting our hiring practices, we can prepare our teams for a future where humans and AI work together to create extraordinary software.
By understanding the impact of AI on software development and the engineering profession, we can stay ahead of the curve and ensure that our organizations continue to innovate and excel in the rapidly changing technological landscape. A critical first step will be hiring people with the right skills for the job!
Redefining the Engineering Interview
Traditional software engineering interviews focus heavily on problem-solving and algorithmic questions, where candidates are asked to write code on the spot, often using a whiteboard. However, with AI code completion tools, the ability to write code quickly from scratch is no longer the most valuable skill. Instead, engineers must excel at understanding the problem domain, evaluating AI-generated code, and fine-tuning it to fit the requirements.
The engineering interview process should reflect these changing priorities. Interviews will focus more on problem-solving and communication skills. While AI shines in tasks involving pattern recognition, large-scale data processing, and repetitive code generation, people, on the other hand, excel in areas requiring creativity, complex problem-solving, understanding of nuanced requirements, and the ability to make strategic decisions based on a holistic view of a project.
Here are key areas to emphasize in the future software engineering interview:
Soft skills: Behavioral interviews and soft skills will become increasingly important. Artificial Intelligence can render technical skills obsolete much more quickly. Soft skills, in contrast, remain relevant and valuable over time, making them a critical asset for long-term success within a company. Software engineering projects have always relied on effective communication, critical thinking, teamwork, and problem-solving to be successful. These skills may have been overshadowed by technical proficiency in the past, e.g., an engineer who is a coding machine but needs to improve at working with others.
As AI tools increasingly automate coding tasks, these so-called “soft skills” gain visibility not because they become more important per se but because the balance of required skills shifts. In this AI-assisted landscape, engineers will spend less time coding and more on tasks requiring human insight, teamwork, and communication. The importance of articulating problem statements, refining requirements, collaborating with team members, and communicating effectively with stakeholders becomes even more apparent.
Breadth of Technical Knowledge: In the future of AI-assisted software development, having a thorough understanding of the various tech stacks, alternatives, and their tradeoffs is essential. To properly direct an AI, engineers must be well-versed in various technology options. With this knowledge, they will be able to better guide AI in producing the best code and selecting the best technologies for particular jobs. As a result, we should evaluate a candidate’s understanding of various tech possibilities and their capacity to use that information strategically when utilizing AI technologies. The system design interview will become increasingly important.
Critical thinking: AI-generated code is not infallible. Engineers must be able to assess the code produced by AI tools critically, identify potential flaws, and provide feedback to improve the AI’s performance. Interviewers should test candidates’ ability to analyze AI-generated code and propose improvements, similar to how candidates are sometimes offered mock code reviews.
Writing code with AI Assistance: As AI becomes more integral to the development process, engineers must work closely with AI tools and adapt to their quirks. Understanding prompt engineering for software engineering and the effective use of tools like Copilot will be critical skills. Interviewers should assess candidates’ experience working with AI code completion tools and evaluate their ability to use them effectively. Imagine a live Ai assisted coding assignment where the engineer has access to these tools, demonstrates how they use them and points out their limitations.
Finally, interviews may also be more personalized. As AI tools become more sophisticated, they can personalize the interview experience for each candidate, guiding the interviewer. This will allow the interviewer to ask about the candidate’s skills and knowledge.
Conclusion
The arrival of AI code completion tools like GPT-4 and GitHub Copilot is ushering in a new era of software development, where AI will soon significantly impact the work of human software engineers. Engineering managers must adapt their hiring and interviewing processes to account for these changes. By focusing on soft skills, breadth of knowledge, critical thinking, and fluency with AI coding tools, we can ensure we hire engineers who can thrive in this new landscape.
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