How We Do Technical Interviews at Distyl

At Distyl, our engineers build production AI systems inside some of the most complex enterprises in the world. The work is forward-deployed: our teams go deep into how a client’s organization operates, sitting directly alongside their people and re-architecting critical systems from the ground up.

Our interviews are designed around the real-world challenges this kind of work involves. We want to see how you’d solve them, and how you work with AI along the way.

Here’s how our technical interviews work, what we’re evaluating, and how to prepare.

What We’re Actually Testing

Every technical challenge in our interview process is drawn from real work our teams do. One of our debugging exercises, for example, started as a bug in a production codebase. We anonymized it and turned it into an interview problem that reflects the complexity we actually encountered.

Our challenges generally involve building or reasoning about AI system components, including guardrails, caching layers, data retrieval systems, and platform infrastructure. Being familiar with the material in Chip Huyen’s Building a Generative AI Platform may be helpful, as it covers the component landscape our interview problems draw from.

We’re evaluating whether you can work within real systems, understand the tradeoffs you’re making, and build things that hold up in production.

The Process

Our technical interview process has several stages, each designed to surface different signals. We typically do a mix of take-home and live coding assessments, calibrated to your specific background and what we want to evaluate.

Take-home assessment. This is typically a backend challenge that takes roughly two hours. AI usage is fully encouraged—use whatever tools you want. We want to see whether you can produce something thoughtful, well structured, and technically sound.

Walk-through with a Distyl engineer. After the take-home, you’ll present your work to one of our engineers. This is where we dig in. We’ll pose hypotheticals: what if this requirement changed, or what if you needed to handle a different edge case? We might point to a specific section of your code and ask you to walk us through the reasoning behind it. If you made a tradeoff, like skipping a guardrail, we’ll want to understand why.

This stage is where we see the biggest differentiation between candidates. Some people produce clean, functional take-homes but can’t explain the decisions behind them. Candidates who operate with an “if it works, it works” mindset tend to struggle at this point, because a correct solution isn’t the bar. You need to understand why it works and be able to reason about how it should evolve.

Codebase review. You’ll be given a codebase you haven’t seen before and asked to review it without AI assistance. We’re looking for whether you can identify best practices, spot issues, and articulate what you’d change and why.

Live coding. Some of our coding screens involve working through a problem in real time. In select screens, candidates use an AI assistance tool that supports the kinds of AI-augmented workflows our engineers use daily. It won’t solve the problem for you. In these sessions, we’re assessing how you interact with AI: your process, your judgment, how you validate and interpret what the tool gives you, and so on. We care less about whether you arrive at the perfect answer and more about how you get there.

Systems and product discussion. We’ll walk through a technical scenario together: how would you approach building this from scratch? How would you decompose this problem? These conversations are about system-level thinking, where you can show us how you reason about compound AI systems rather than individual model calls or isolated code.

More Than Technical Skill

Distyl’s engineers are embedded with clients, working on problems where they don’t always have full visibility into how a system operates. They need to think on their feet, communicate clearly under pressure, and care deeply about the client’s problem.

Our interviews reflect that. In some stages, we present case studies drawn from the kinds of challenges our teams navigate in practice. You won’t have all the information. We want to see how you reason through ambiguity, how you communicate your thinking, and whether you default to honest assessment over performative confidence. “I don’t know, but here’s how I’d find out” is a better signal than overcommitting to an answer you’re unsure about.

We’re also looking for ownership. Distyl is a place where people get excited about picking up things that feel beyond their reach. People who thrive here learn fast and are hungry to keep learning. That quality shows up in interviews, and we try to assess it alongside technical ability.

How to Prepare

Here’s what we’d tell you before your interview:

Understand AI system components. Our interview challenges involve building and reasoning about the kinds of systems we actually deploy. The more familiar you are with the component landscape we mentioned earlier, the more prepared you’ll be.

Be ready to explain your decisions. If you use AI to help with the take-home (and you should), make sure you understand every line of what you submit. We will ask you to explain it, modify it, and consider alternatives. Show us how you think.

Practice thinking at the system level. We care about how you design systems, not just how you implement individual functions. Given a complex requirement, how would you break it into components? What would you build first? What tradeoffs would you make?

Be yourself. Show us your perspective on how you build things, including how you work with AI. Give us your real opinion, not the answer you think we want to hear. And be honest about what you don’t know: what matters here is the drive to figure it out.

If this sounds like the kind of place you want to build, see our open roles here.