How AI is Shaping Software Engineering Job Interviews

Discover how AI is transforming the software engineering job market, changing interview styles and expectations for candidates.

AI has fundamentally altered what it means to be a software engineer, and it’s also changing the pathways to enter the field.

In the pre-AI era, software engineering interviews focused predominantly on coding from memory, solving algorithmic puzzles, and showcasing technical speed under stress. Nowadays, CEOs are diving into platforms like GitHub and X to discover hidden talent, urging candidates to leverage AI during interviews, and increasingly prioritizing judgment and taste.

This shift has led to a drastically different job market for software engineers across the globe. Le’ale Addison, a fresh computer science graduate who has interned at Amazon and KPMG, speaks to how this transformation has impacted her own job search. In recent interviews, she was bombarded with questions about AI. Interviewers wanted to know about her familiarity with machine learning, natural language processing, and how she integrated AI into her existing workflow. “Those questions weren’t previously asked,” she remarked.

Addison’s experience is indicative of a broader industry recalibration, with companies such as Dropbox and Cisco now requiring engineers to demonstrate their AI competence during the hiring process. The period dubbed “The Great Coding Reset” has examined how coding has evolved, exploring new tools that contribute to a paradoxical productivity scenario and the power struggles within AI-driven office environments.

Many job seekers are discovering that while hiring is on the rise, a staggering 74% of developers face difficulties in securing positions, according to HackerRank’s 2025 report.

Business Insider consulted with various tech firms, AI startups, and career coaches to address the pressing question: What does it take to land a software engineering job when AI is extensively automating coding tasks?

Big tech companies like Google and Meta are fiercely competing for top AI engineering talent, offering astonishing salaries and substantial computational resources. On the flip side, startups have devised innovative strategies to attract potential hires. Executives from AI coding startups such as Cognition, Base44, and Replit frequently scout for engineers by browsing posts on X and GitHub, where developers showcase their latest endeavors.

Replit’s chief people officer, Stacey La Torre, noted that X has become a go-to medium for recruitment, involving rank-and-file employees in the talent search as well. They have launched a Slack channel titled ‘Talent Spot,’ allowing employees to share leads from networking events or personal connections.

Emily Cohen, the head of people and operations at Cognition, emphasized that every employee plays a role in scouting talent. She has gone so far as to travel extensively to persuade promising engineers to join her startup. “Just last week, I drove a candidate to the airport because I wanted to be the last person they talked to before they left San Francisco,” Cohen explained.

The evolution of recruiting has also affected how interviews are conducted. The previous standard of coding tests via LeetCode is no longer the sole criterion; today, candidates might also engage in work trials where they perform tasks alongside prospective teams. This gives both parties a chance to assess fit, with startups like Lovable, Cursor, and Kilo adopting this approach.

Xavier Contreras, the head of data engineering at a hedge fund, shared that interviews where he had to tackle skill challenges have transformed dramatically compared to five or six years ago. Instead of merely solving coding problems, interviewers now expect insights on project architecture and decision rationale. With the aid of AI in delivering take-home assignments, he noted that tasks that once demanded a month’s effort are now expected to be completed in three days.

As AI takes on more of the standard workload, employers are focusing on candidates’ comprehension of technology and their capability to intervene during errors. Soft skills like systems thinking and problem-solving are now among the most sought-after abilities, according to Erin Scruggs, LinkedIn’s head of global talent acquisition.

Scott McGuckin, VP of global talent acquisition at Cisco, commented that the company is moving to project-based assessments rather than traditional coding tasks. They are also incorporating AI-assisted development environments into their interviews to assess candidates in real-time, ensuring that human oversight remains crucial in this evolving landscape.

Sundeep Teki, a career coach aiding placements at pioneering AI labs, noted that the increasing technical standards for jobs at companies like OpenAI and Anthropic signify the rising complexity in the field. Alongside technical expertise, employers are valuing cultural fit more than ever. At Anthropic, for example, candidates face a unique non-technical culture interview essential for roles there.

As hiring practices adjust, the nature of engineering roles has also transformed, with many elements of traditional developer duties now automated. Companies are on the lookout for versatile candidates who can manage multiple functions, as evidenced by Contreras’s observation regarding the blend of software engineering, data analytics, and data science.

Ultimately, firms are in search of “data unicorns,” exceptionally skilled individuals whose breadth of knowledge spans across several areas. “It’s a lot more hectic,” he remarked, indicating the need for more comprehensive expertise in today’s tech landscape.