You'll build 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. As a Machine Learning Engineer, you will own outcomes end-to-end on the team extending ZoomInfo's B2B data graph into the long tail of companies with little public information, resolving entity identity at scale, and reading buying intent from meaning rather than keywords. You will apply classical machine learning, statistics, or modern language models, whichever the problem calls for, with a short path from decision to production.

What You'll Do

• You will extend ZoomInfo's data graph into the long tail of companies with little public footprint, extracting leadership, locations, and products from company websites and detecting stale records.
• You will predict what the graph doesn't know yet, estimating headcount and revenue for under-documented companies using gradient-boosted trees, regression with missing inputs, and calibrated uncertainty.
• You will determine whether two records describe the same company or person, measuring both wrongly merged and wrongly split outcomes.
• You will infer buying intent from the meaning of web content across large volumes of multilingual, noisy text, feeding propensity scoring, lookalike retrieval, and contact recommendations.
• You will build agents that research companies and cite their sources, and design the evaluations that separate a correct result from a run that merely finished.
• You will distill large models into smaller ones that run cost-effectively across the full dataset, owning quantization and serving as part of the same work.
• You will take ambiguous, high-stakes problems from undefined to shipped, and raise the team's engineering bar through review, design, and mentorship.

What You Bring

Must-Have:

• You have taken machine learning systems to production and owned them after launch, following the outcome into whichever layer it needs, with technical leadership as an individual contributor: setting direction for a problem area, design review, and mentoring. Depth matters more than years.
• You bring classical machine learning expertise beyond language models, including supervised learning and feature engineering on large, messy tabular data.
• You apply statistics to decisions: experiment design, statistical inference, and calibrated scores under class imbalance.
• You have deployed language processing at scale — text classification, information extraction, and entity linking over large volumes of multilingual, noisy text.
• You have built and operated LLM agents or multi-step systems in production, including tool and context design and failure analysis from traces, with evaluation for systems with no single right answer using LLM judges validated against human labels.
• You are proficient in production Python and strong SQL with distributed data processing experience, and you use AI coding tools daily with rigorous review of their output.

Preferred:

• You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.
• You bring propensity modeling, clustering, or entity resolution experience on messy, real-world data.
• You have trained and served open-weight models in PyTorch or an equivalent framework, tracking cost per unit of work.
• You have defended LLM systems against adversarial inputs and prompt injection, or worked on web-scale information extraction, knowledge graphs, or user memory for agents.

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