The Four Traits of Great AI Engineers

Hiring great AI engineers is one of the hardest problems in tech. Not because there’s a shortage of people with relevant skills—though that’s part of it—but because most hiring processes don’t filter for what actually matters. Instead, they optimize for people who look good on paper.

Over the years, I’ve noticed that the engineers who truly move the needle share four traits. Surprisingly, these traits are often absent from standard hiring checklists. If you want to build a team that creates real impact, you need to look beyond résumés and interviews and focus on these characteristics.

1. Deep Expertise

The first and most obvious trait is mastery of a core skill.

Great AI engineers have deep expertise in either data/AI or software engineering. Those from the data side might have backgrounds as data scientists, machine learning engineers, or DevOps professionals. Those from the software side often come from full-stack or traditional engineering roles.

What matters is that they’ve mastered at least one of these foundational domains. Without this, they lack the depth to tackle the complex challenges that come with building AI systems.

2. Practitioners at Heart

The best engineers are always building.

Even when they’re off the clock, they’re working on side projects, contributing to open source, or hacking together solutions to problems they encountered last week. It’s not about the specific projects; it’s about the habit of creation.

This relentless curiosity does more than keep them sharp. It accelerates their learning and helps them recognize patterns and problems faster. It also makes them invaluable team members.

In fact, I think most companies get this wrong. Many don’t even ask about side projects, and some actively discourage them. That’s a mistake.

At Palindrom, we do the opposite. We encourage side projects and create an open culture where people can share what they’re working on and the lessons they’ve learned. This kind of environment not only keeps engineers engaged but also raises the learning rate for the entire team.

3. Product Thinking

This trait separates good engineers from great ones: product thinking.

The best AI engineers care deeply about the customer and the problem they’re solving. They know it’s not just about building something that works—it’s about building something that matters.

Engineers with product thinking make different decisions. They consider the user experience, trade-offs, and long-term impact of their work. These small decisions compound over time, leading to significantly better outcomes.

Without this mindset, engineers often default to optimizing for technical performance at the expense of usability. But with it, they make every line of code count.

4. Momentum Creation

The rarest—and most valuable—trait is what I call momentum creation.

Some engineers thrive in ambiguity. Give them a rough idea and a few resources, and they’ll create a first version that works. It won’t be perfect, but it’ll be good enough to prove the concept and get the ball rolling.

Most engineers can’t do this. They get stuck waiting for clarity, roadmaps, or permission. Momentum creators, on the other hand, generate progress where there was none. They’re the ones who turn ideas into reality.

This trait is hard to find, but when you do, it’s transformational.

The Challenge with Traditional Hiring

Here’s the catch: these traits are nearly impossible to test for in traditional interviews.

Résumés won’t tell you if someone thrives in ambiguity. Coding challenges won’t show you their product thinking or whether they’re natural momentum creators. The best way to evaluate these traits is to look at what candidates have built and spend time working with them.

At Palindrom, we’ve refined our hiring process to include a one-week collaboration period with candidates. During that time, we test how they handle ambiguity, how they generate momentum, and how they think about the user. This approach has consistently helped us identify the kind of engineers who drive real impact.

Closing Thoughts

If you want to hire great AI engineers, stop focusing on credentials and start looking for builders. Look for deep expertise, a practitioner’s mindset, product thinking, and the ability to create momentum. These are the traits that separate the people who can talk about AI from the people who can actually build it.

Most companies won’t bother to do this. That’s precisely why it works.

Gabor