Developer Quality & Vetting Framework
Rebuilt Turing's engineer vetting pipeline — end-to-end evaluation of technical skills, communication quality, and client-readiness across tens of thousands of engineer applicants.
At Turing’s scale, the quality of the engineer supply side is a product problem, not just an operations problem.
The problem
The original vetting process was a mix of manual review and early ML models that hadn’t been systematically updated as the engineer pool and client requirements evolved. Pass rates and client satisfaction scores were moving in opposite directions.
What we built
A structured vetting framework with tiered technical assessments calibrated by role and seniority, structured evaluation of async and sync communication quality, and a composite client-readiness score that combined technical and behavioral signals.
What I learned
Quality systems at scale require constant calibration. A vetting framework that was accurate six months ago will drift as the applicant pool changes and client requirements evolve. Building in feedback mechanisms to catch that drift early is as important as getting the initial design right.