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. com .

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). Proficient in Python and able to write clean, modular, testable code .

Experience developing and deploying ML solutions in a cloud environment , especially AWS. Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch. Independent engineer who can own work from problem framing → experimentation → implementation → rollout .

Excellent written and spoken English . Enjoy working closely with engineers, product partners, and other data scientists. Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.

Master’s degree in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), or equivalent practical experience .

Nice To Have

: Experience with scikit-learn, PyTorch, TensorFlow, XGBoost , or similar modeling stacks. g. MLflow, W B).

Experience with SQL , data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark . Exposure to feature stores , embedding pipelines, or vector search for retrieval-based systems. Experience building HTTP/gRPC APIs or lightweight services around model inference.

g. GitLab CI ). Experience in agile , remote and async team environments.

Publications, patents, Kaggle/competition results, or open-source ML contributions.

About This Role

: Hands-on modeling work with room to explore, benchmark, and improve real systems. Collaboration on ML patent submissions and participation in weekly ML / research paper review meetings. A multicultural, engineering-focused team with strong peer support.