Case studies

The company needed engineers who could build AI products end to end, but strong candidates often came from ML, data science or research backgrounds rather than carrying a “Full Stack AI Engineer” title.

We looked beyond job titles, assessing genuine ML and data foundations alongside software engineering depth and the ability to build in an early-stage startup environment.

The role required strong engineering depth alongside client-facing communication, regular travel to San Francisco and the ability to operate in a 0→1 environment.

We looked beyond engineering background alone, assessing communication, customer-facing judgment and startup ownership.

The company was scaling its engineering organization globally from around 30 to 200 people, hiring across multiple countries while maintaining a high technical bar for large-scale distributed systems and strong company fit.

We combined market-specific sourcing with consistent assessment across locations, focusing on technical depth, distributed-systems experience and the ability to succeed in the company’s operating environment.