[Hiring] Principal Machine Learning Engineer @ZoomInfo Technologies LLC

Role Description

You'll set the technical direction for the intelligence layer 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 senior individual contributor with no direct reports, you will own strategy across the data graph, entity resolution, and buying intent, staying hands-on in the hardest of these problems. You'll decide when classical machine learning, statistics, or language models is the right tool, and turn good judgment into standards other teams adopt.

What You'll Do

• Set technical strategy for the data graph, entity resolution, buying intent, and agent context.
• Frame and ship problems nobody has framed yet, such as extraction for companies with no public footprint or semantic intent at web scale.
• Define evaluation standards that other teams adopt, including sampling methodology, human labels, validated LLM judges, and release gates.
• Define the data contracts between the data graph, intent signals, and the agents that consume them.
• Turn good choices into defaults across the group: the baseline every model must beat, how uncertainty is reported, and which metrics get retired when they mislead.
• Lead design reviews, mentor senior engineers, shape hiring, and represent the team's technical judgment to product and leadership.

Qualifications

• Set technical direction as an individual contributor for a problem area spanning more than one team.
• Breadth across classical machine learning on large, messy tabular data, applied statistics, language processing at scale, and LLM agents or multi-step systems.
• Defined and driven adoption of evaluation standards beyond your own team.
• Track record of changing how a group builds or measures.
• Made cost and capacity decisions for model training and serving.
• Hands-on in production Python and strong SQL with distributed data processing experience.

Requirements

• Experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.
• Entity resolution, knowledge graph, or web-scale information extraction experience on messy, real-world data.
• Trained and served open-weight models in PyTorch or an equivalent framework.
• Built user memory or context systems for agents, or defended LLM systems against adversarial inputs and prompt injection.

Benefits

• Comprehensive benefits including holistic mind, body, and lifestyle programs designed for overall well-being.
• Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training.
• Base salary for this position ranges from $192,500 to $302,500 USD.