Director, Applied AI

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, from training data through model serving, blending classical machine learning and data science with LLM and agentic systems. You'll set the technical bar for a small, senior team while staying hands-on, shipping code and prototypes alongside the engineers you lead.

What You'll Do

• You will extend ZoomInfo's B2B data graph into the long tail, making it the most comprehensive and accurate resource available, including companies with little public footprint.
• You will lead the team's work on agent memory, distinguishing what a user has supplied from what a system of record already owns.
• You will choose the right method for each problem — classical machine learning, language models, or code — based on measured evidence, and stop work that won't pay off.
• You will define the evaluation bar for the team's models and agents, building evaluation datasets, regression gates, and experiment designs that make quality claims trustworthy.
• You will own inference cost, latency, and capacity as core disciplines alongside model quality, including build-versus-buy and distillation decisions.
• You will hire and develop machine learning engineers, data scientists, and research engineers, growing senior engineers into technical leaders.
• You will represent the team's work across product, platform, security, and legal, and present results and their limits clearly to executives.
• You will set the standard for how the team uses agentic coding tools, pairing precise specifications with rigorous code review.

What You Bring

Must-Have:

• You have significant, demonstrated experience building and shipping production machine learning systems, and you lead by building alongside your team — capability matters more than years.
• You have a track record of hiring and developing senior machine learning engineers and data scientists against a high bar.
• You stay hands-on today: shipping code, prototyping independently, and using agentic coding tools daily with rigorous review.
• You bring deep classical machine learning and data science expertise (supervised learning, feature engineering, statistical inference, experiment design, strong SQL) alongside production LLM and agentic systems, with the judgment to choose between them, including setting evaluation standards such as leakage-safe validation and calibration.
• You have owned inference cost, latency, and capacity alongside model quality, including build-versus-buy decisions, and you communicate results and their limits clearly to executive stakeholders.

Preferred:

• You bring entrepreneurial experience — founding a company, or taking a product from inception to paying customers as a founding or early engineer.
• You have experience in propensity modeling, ranking and retrieval, clustering, or entity resolution at scale.
• You have worked on web-scale language processing over multilingual, noisy text, knowledge graphs, or user memory for agents.
• You have experience with post-training and distillation, open-weight model serving, or AI governance and safety practices (ISO/IEC 42001, NIST AI RMF).

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