A senior engineer's greatest impact often comes not from being the deepest technical expert, but from having enough context across multiple domains (marketing, PR, engineering) to act as a translator. They synthesize information and help teams with deep expertise navigate complex, cross-functional decisions.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

When building his internal developer tools team at Meta, Adrian's hiring strategy was simple: find talented engineers who were already building similar tools on the side out of passion or necessity. He then offered them the chance to turn that side-hustle into their full-time, high-impact job.

With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.

Building your own product forces you to confront technical realities like database migrations and architectural trade-offs. This firsthand experience provides deep empathy for engineering challenges, which in turn builds crucial credibility and improves collaboration with development teams.

A common hiring mistake is prioritizing a conversational 'vibe check' over assessing actual skills. A much better approach is to give candidates a project that simulates the job's core responsibilities, providing a direct and clean signal of their capabilities.

For high-level leadership roles, skip hypothetical case studies. Instead, present candidates with your company's actual, current problems. The worst-case scenario is free, high-quality consulting. The best case is finding someone who can not only devise a solution but also implement it, making the interview process far more valuable.

The creator of Claude Code prioritizes hiring generalists who possess skills beyond coding, such as product sense and a desire to talk to users. This 'full-stack' approach, where even PMs and data scientists code, fosters a more effective and versatile team.

In regulated industries where projects "take a village," the most crucial skill is not raw engineering talent, but communication. The ability to align a team, share ideas, and ensure mutual understanding is paramount, as a single dropped ball in communication can derail an entire product launch.