For decades, entertainment hiring has been driven by who you know—an outdated, insular model that Thread set out to transform. This AI-powered platform reimagines how production teams discover and evaluate talent, leveraging deep-learning algorithms to match candidates based on experience, skills, and industry connections rather than personal networks. I shaped the platform’s UX and interface, ensuring that AI-driven searches felt intuitive and effortless. I lead a team who crafted an experience that balanced intelligence with accessibility, making complex machine-learning outputs feel seamless and actionable for users. Designed before AI hiring tools like Google Gemini entered the public sphere—Thread was ahead of its time, pioneering a new standard for talent discovery.

Beyond just being intuitive, the design needed to address a critical industry challenge: accessibility and bias in hiring. From dynamic talent storefronts that showcased candidates’ work in compelling, structured ways to bias-aware search filters that mitigated unconscious favoritism, every interaction was built with fairness and transparency in mind. The result wasn’t just a more efficient hiring platform, but a tool that actively reshaped industry norms—promoting equity while streamlining the hiring process for production teams worldwide. By making talent discovery faster, smarter, and more inclusive, Thread redefined what it means to hire in the entertainment industry, proving that AI can be a force for both efficiency and progress.