Whiteboard algorithms tell you nothing about production engineering. Our process tests architecture judgment, code quality under review and AI-readiness on real cases.
THE PROBLEM
Before explaining how ours works, you need to understand why standard processes produce bad engineers.
Solving a graph tree in 45 minutes has zero correlation with maintaining an API that handles 50,000 requests per hour. They are different skills that don't overlap.
A recruiter can verify if a candidate says the right words. Only an active CTO can assess whether an architecture decision is correct or a mistake that will cost months.
Remote work requires specific communication, documentation, and autonomy skills that don't appear in any standard in-person interview.
In 2026, an engineer who doesn't master AI tools delivers 30% to 40% slower than one who does. Almost no process evaluates this.
5 PILLARS
Real scenarios with real constraints: scale, budget, team size, existing technical debt. We evaluate reasoning about trade-offs and the ability to communicate technical decisions to non-technical stakeholders.
We review the candidate's real code — something built in production, not an interview exercise. We look at clean structure, error handling, testing discipline, separation of concerns, and readability.
We evaluate effective use of GitHub Copilot, Cursor, Claude, and similar tools. Prompt engineering applied to real engineering tasks. And most importantly: judgment for knowing when AI output needs human review.
Written clarity, verbal fluency, ability to raise issues proactively, and timezone discipline. Remote work doesn't fail due to lack of technical skill — it fails when communication breaks down.
Employment verification, real professional references, and cultural alignment with startup and scale-up environments. We look for engineers who have worked on products with real users.
THE FUNNEL
Each stage eliminates profiles that don't fit — not people, but profiles that couldn't deliver in the context of our clients.
100% of candidates enter the process.
40% pass. Initial screening of experience, stack, and production work.
20% pass. Written clarity, verbal fluency, timezone discipline.
12% pass. Pair programming with an active CTO on real problems.
10% pass. Employment history, confirmed professional references.
Final 4%. Ready to deploy on client projects.
final acceptance rate