ML Ops Engineer, Zeta Global

• 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
• We’re especially interested in candidates who are technically strong, intellectually curious, and motivated by difficult, ambiguous problems where good data science and solid engineering both matter

Desirable

• 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
• 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