How We Do Technical Interviews at Distyl
At Distyl, our interviews are designed around the real-world challenges our teams solve. Here’s how our technical interviews work, what we’re evaluating, and how to prepare.
Our engineers have always been accountable for outcomes, not just code. AI has changed what it takes. Now that an engineer can take on more of a system than ever before, the bigger challenge is the decisions they have to make. Some are familiar, like which tradeoffs to accept and what to build when the problem isn’t clear yet. Others are newer, like how to structure a codebase that agents will keep building on.
We’ve redesigned our technical interview process for how our engineers work today. Here’s how the new process works, 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. 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 process now moves quickly through three stages: a conversation with a recruiter, a technical round, and an onsite at our office. What each stage looks like depends on the role and your experience, but the shape of the process is the same for everyone.
Recruiter conversation. We’ll talk about the role, your technical background, what you’re looking for in your next job, and logistics.
Technical round. Depending on your background, this is either a technical deep dive with a member of our engineering leadership or a live coding session with a senior engineer.
- For candidates in the live coding session: you’ll spend the first half working through a problem in real time with a coding agent built into the interview environment. We’re assessing how you interact with AI, including how you validate and interpret what the agent gives you. We care less about whether you arrive at the perfect answer and more about how you get there. In the second half, you’ll review a pull request without AI assistance. We want to see what you’d change and why.
The onsite. The biggest change from our previous process is the onsite, most of which is a working session built around an ambiguous problem. Whether you’re an IC or a leader, it’s designed to show us how you’d take on a project as an engineer at Distyl, from scoping to shipping.
First, you’ll walk through your thinking with one of our engineers: how would you decompose the problem? What would you build first? This conversation is about system-level thinking, where you can show us how you reason about AI systems rather than individual model calls or isolated code.
Then you’ll build the solution, with AI. This working session is where we see the biggest differences between candidates, both in the technical judgment behind what they build and in how well they understand it. An “if it works, it works” approach isn’t enough. You should understand why your solution works and be able to explain how it should evolve.
You’ll also have lunch with us at the office and get to know the people here. Selected candidates will be invited to a short conversation with one of our founders, usually within a day or two of the onsite.
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 and care deeply about the client’s problem.
Our interviews reflect that. Some of them include case studies drawn from the kinds of challenges our teams navigate on real projects. You won’t have all the information. We want to see how you reason through ambiguity 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 components we mentioned earlier, the more prepared you’ll be.
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 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.
