ZoomInfo Technologies LLC: Director, Applied AI

About this role

Headquarters: Bethesda, Maryland, United States; Remote-US-MA; Remote-US-MD; Remote-US-WA; Vancouver, Washington, United States; Waltham, Massachusetts, United States ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute.

You’ll make things happen–fast. You'll lead the team that builds the intelligence every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. This role owns the B2B data graph strategy end to end, blending classical machine learning and data science with LLM and agentic systems, and treating evaluation and inference cost as core disciplines. You'll set the technical bar for a team of senior engineers while staying close to the code, shipping alongside the people you lead.

What You'll Do You will ship code alongside your team, prototype alone to prove or kill an idea, and review code as a peer, setting how the team uses agentic coding tools with precise specifications and rigorous review. You will own delivery end-to-end, from problem framing through serving and on-call, including long-tail graph coverage and user memory for agents that separates user-supplied context from system-of-record data.

You will choose the right method for each problem — classical machine learning, language models, or code — for challenges like sparse-company revenue estimation, entity resolution, and semantic intent modeling, deciding on measured evidence and stopping work that won't pay off. You will define what it takes to claim an agent's output is correct, not just that its run completed, building the evaluation datasets, regression gates, and experiment designs that back those claims.

You will own inference cost, latency, and capacity, including build-versus-buy and distillation decisions, since a model too expensive to run everywhere isn't a result. You will hire and grow machine learning engineers, data scientists, and research engineers, developing senior engineers into technical leaders. You will work across product, platform, security, and legal, and present results and their limits to executives, including when a system isn't good enough to launch.