ML Ops Engineer

WHO WE ARE Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. The Role We’re looking for a skilled ML Engineer / Data Scientist with 3+ years of software or applied ML experience to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on AWS.This role sits at the intersection of data science and engineering: exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work—not just notebooks, and not just infrastructure. Who you are: Strong foundation in machine learning, statistics and experiment design. Experience building models for real business or product problems, not only academic benchmarks. Comfortable working with structured and unstructured data: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline. Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability). Interest in modern ML, including classical ML, deep learning, and LLM / GenAI workflows where relevant (fine-tuning, RAG, evaluation, prompt/versioning). P roficient in Python and able to write clean, modular, testable code. Experience developing and deploying ML solutions in a cloud environment, especially AWS.