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). 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. Familiarity with  ML experiment tracking  and reproducibility (e.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. Working knowledge of  Docker , basic orchestration, and CI/CD (e.g.  GitLab CI ). Experience in  agile ,  remote and  async  team environments. Publications, patents, Kaggle/competition results, or open-source ML contributions. What you might like 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. High trust and autonomy —clear goals, freedom in how to reach them. Internal product impact : meaningful projects that improve developer and user experience, not endless maintenance tickets. Short approval cycles  and solid product partnership. A  healthy meeting policy  and emphasis on protecting focus time. Flexible hours , remote/home office options, and a calm, engineers-only office when on-site. Competitive compensation , including stock options.   We’re hiring across multiple levels . Title, scope, and compensation depend on experience—from strong applied ML generalists to senior people who can lead modeling direction and mentor others. We’re especially interested in candidates who are  technically strong, intellectually curious, and motivated by difficult, ambiguous problems  where good data science and solid en