Senior Machine Learning Engineer

You'll help 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. You'll own outcomes end to end, from the data a model learns from to the product surface where its output lands, applying classical machine learning, statistics, or language models based on what the problem calls for. You'll take a problem in the data graph, entity resolution, or buying intent from design through production, and own it after launch.

What You'll Do

• You will extract leadership, locations, and products from the websites of companies with almost no public footprint, and detect stale records, choosing which pages to read based on cost per document.
• You will estimate headcount and revenue for companies with sparse public information, 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 false merges and false splits.
• You will infer buying intent from the meaning of what companies read, not keyword matches, across large volumes of multilingual, noisy text, feeding propensity scoring, lookalike retrieval, and contact recommendations.
• You will build research agents that cite their sources, along with the evaluation systems behind them: human labels, validated LLM judges, and regression gates.
• You will train small, task-specific models distilled from larger ones, measuring them against the larger model rather than against perfection, and own their quantization and serving.
• You will take a problem from design to production and own it after launch, including its evaluation and its cost, while helping other engineers improve their work through design and code review.

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 — 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, along with applied statistics: 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 LLM agents or multi-step systems in production, including tool and context design and failure analysis from traces, and you've evaluated systems with no single right answer using LLM judges validated against human labels.
• You are proficient in production Python and strong in SQL, with experience in distributed data processing, and you use AI coding tools daily, rigorously reviewing 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 experience in propensity modeling, clustering, or entity resolution on messy, real-world data.
• You have trained and served open-weight models in PyTorch or an equivalent framework, with cost tracked 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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