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.

What You Bring

Must-Have:

• You have significant experience building production machine learning systems and leading the engineers who build them by shipping alongside them — demonstrated 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, building prototypes 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 and experiment design, strong SQL) alongside production LLM and agentic systems, with the judgment to choose between them, including setting an evaluation bar with leakage-safe validation, calibration, and validated LLM judges.
• You have a record of cost and capacity decisions for model serving, such as moving a workload from a hosted model to a distilled or self-hosted one, with the savings measured, and you report results to executives with their limits stated, including recommending against a launch.

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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