Case Study
We Sell Technical Hiring Software. Of Course We Use It to Hire Our Own Developers.
Call it dogfooding. Being our own best customer. Practicing what we preach. Whatever you want.
At CoderPad, we help companies figure out who can actually do the job without relying on resumes, trivia questions, or gut feel. So when we’re hiring developers ourselves, the bar is pretty obvious: our own process should be one we’d recommend to our customers.
Hiring great developers is one of the biggest levers we have on how fast we can ship. Our hiring process has to help us find exceptional people quickly — without wasting engineering time or lowering the bar.
For us, that means getting meaningful signal early, giving candidates realistic work, letting them use AI, and evaluating the full set of skills that make someone a great engineer: technical ability, problem solving, systems thinking, product judgment, and collaboration with both LLMs and humans. It also means being deliberate about where we spend human time in the interview process. We don’t want five engineers interviewing someone to discover something we could have learned much earlier.
Across our global engineering organization, we use Qualify, Screen, and Interview to do exactly that.
Qualify: don’t waste the first call on the basics
Our hiring process starts before anyone gets on the phone. We don’t want to spend 30 minutes learning something we could have figured out in 30 seconds.
Every applicant completes a short CoderPad Qualify pre-screen covering the fundamentals: Why CoderPad? Which programming languages are you proficient in? What relevant experience do you bring?
We define what we’re looking for upfront and use those responses to decide who moves forward. That means recruiters spend less time establishing basic fit and more time having the conversations that actually benefit from a human.
Every unnecessary recruiter screen slows the process down. Qualify lets us filter earlier, focus our team on the candidates with real potential, and move the right people through faster.
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Screen: enough about your resume. Show us.
A resume tells us where someone has worked. CoderPad Screen shows us how they work.
Candidates complete a role-relevant Project before moving into live technical interviews. At this point in the funnel, we still have a lot of candidates and a finite amount of engineering time. Screen helps us get meaningful skills signal at scale so we can identify the people we genuinely want to spend more time with.
And no, we're not asking candidates to invert a binary tree for fun.
They work in a full development environment where they can navigate multiple files, understand existing code, debug, install packages, work with databases, and use AI. The exact Project changes based on the role: candidates may hunt down bugs in an existing application, get up to speed on an unfamiliar codebase, or build new functionality.
We're not just grading the final code.
Follow-up questions help us understand how candidates got there: how they framed the problem, why they made certain decisions, what tradeoffs they considered, and what they'd change. That gives us early signal on problem solving, technical judgment, communication, and whether someone can explain and defend their decisions — not just whether their code passes.
AI is enabled, too, because developers use AI at work. We want to see whether candidates can use it to move faster and do better work without outsourcing their judgment to it.
Engineering interview time is expensive. Screen means we’re not spending it figuring out whether someone can do the job — we’ve already seen evidence that they can. We can interview fewer people, go much deeper with them, and make better decisions.
Interview: okay, now let's work together
Screen helps us narrow the funnel and decide who warrants more of our engineers' time. Interview helps us make that time count.
Every developer candidate works collaboratively with one of our engineers in CoderPad Interview. We're always in the code together, and we're always evaluating much more than whether someone can produce a technically correct answer.
We want to understand what it would actually be like to build with this person. Can they reason about the system around the problem rather than just the task in front of them? Do they have the product taste to distinguish between what can be built and what should be built? Can they communicate their thinking, take input, challenge an assumption, and adapt when the problem changes? Can they use AI to make themselves better without letting it replace their own judgment?
Those are engineering skills.
The technical problem itself changes based on the role. A candidate may design a system on the whiteboard and then get into the implementation details, navigate an unfamiliar codebase, debug an application, extend an existing feature, or work through a frontend, backend, or database problem.
But the way we work together is consistent. We introduce ambiguity and pay attention to the questions candidates ask before jumping to a solution. We change constraints to see whether they can zoom out, understand the broader system, and adjust their approach. We discuss tradeoffs to understand their judgment and product taste, and we collaborate throughout to see how they communicate when another engineer is actually in the room.
AI is part of every technical interview, too. In 2026, communicating well as an engineer increasingly means communicating well with both humans and LLMs. We look at how candidates direct AI, what context they provide, how they iterate, what they trust, what they challenge, and whether they recognize when the output isn't good enough. The question isn't whether they use AI. It's whether they use it with judgment.
Interview also lets us connect signals across the process. We can pick up something from a candidate's Screen Project and push it further: Why did you build it this way? What breaks at 100x the scale? What would you refactor? What would you do differently if the customer need changed?
The exercise should reflect the job. So should the skills we're evaluating.
By the time someone gets here, we already know they can code. The live interview answers the harder question: do we want this person solving hard problems alongside us? That’s the confidence we need to make a great hire.
One hiring decision. Multiple signals.
By this point, we've already learned a lot about how someone codes, thinks, communicates, collaborates, exercises judgment, and solves problems. We don't save those things for a separate “soft skills” interview because they're part of being a good engineer, and we're evaluating them throughout the process.
Once the hands-on technical work is complete, we add different perspectives.
Candidates have a cross-functional interview with a Product Manager to go deeper on product thinking and how they collaborate with people outside engineering. They complete a structured behavioral problem-solving exercise with a member of our executive team to explore how they approach ambiguity, work with others, and demonstrate CoderPad's values. And they have a leadership conversation with our CEO focused on ownership, judgment, ambition, and the impact they can have here.
Each conversation has a purpose and adds a different signal. The goal isn't to collect as many interviews as possible; it's to build enough evidence that no single coding exercise, interviewer, or gut feeling gets to make the hiring decision.
Yes, we eat our own dog food
We could end this with something polished about being our own best customer.
But really: we sell technical hiring software. We should use the hell out of it.
Using CoderPad ourselves means we experience the same process our customers and candidates experience, and what we learn goes straight back into how we hire, what we build, and how we advise customers. More importantly, it forces us to practice what we preach: get signal early, make the work realistic, evaluate the skills the job actually requires, let candidates use the tools they'll use at work, and make decisions based on evidence.
That's our advice to customers, and it's how we hire our own developers.
Dogfooding isn’t valuable because we get to say we use our own product. It’s valuable because it creates a feedback loop. Every developer we hire teaches us something about our hiring process, our candidate experience, and the product itself. That’s how both get better.