Teams at hud

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Tell HUD's story and evangelize vision; source and meet candidates, AI engineers, and ecosystem partners; define culture, screen and onboard hires; build recruiting ops and tooling; run events and content; collaborate with marketing, Chief of Staff, and GTM engineers to drive introductions and community growth.

Build and optimize HUD's comprehensive evaluation framework for Computer Use Agents (CUAs) at scale, ensuring high performance and reliability.