Zeta Global

Zeta Global provides an AI-powered marketing cloud that enables enterprises to acquire, grow, and retain customers through precision marketing, leveraging data science, advanced analytics, and machine learning to create optimized customer experiences.

Media

Consumer Discretionary

1K-5K (1434)

Founded 2007

19 open positions

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ML Ops Engineer

2 months, 2 weeks ago

Germany

Full-time

Mid Level

Machine Learning Engineer

Software Development

Agile Apache Airflow Apache Spark AWS CI/CD dbt Deep Learning Docker Generative AI GitLab CI gRPC LLM Machine Learning MLflow Python PyTorch Scikit-learn SQL Statistics TensorFlow XGBoost

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Zeta Global

Media

1K-5K

Founded 2007

View All Jobs 19

Description

Design, build, and improve machine learning solutions in a cloud environment, primarily on AWS.
Explore data, develop models, and run rigorous experiments to solve real business and product problems.
Bring the best approaches into production with a reliable, reproducible workflow.
Work across the full ML lifecycle from problem framing through experimentation, implementation, and rollout.
Perform feature engineering, dataset construction, labeling quality checks, and train/validation/test discipline.
Compare modeling approaches using clear metrics, error analysis, and tradeoff analysis.
Package models, build inference paths, monitor performance, and iterate after launch.
Collaborate closely with engineers, product partners, and other data scientists.
Contribute to ML patent submissions and participate in weekly ML and research paper review meetings.
Lead modeling direction and mentor others at higher experience levels.

Requirements

3+ years of software or applied machine learning experience.
Strong foundation in machine learning, statistics, and experiment design.
Proficient in Python and able to write clean, modular, testable code.
Experience building models for real business or product problems.
Comfort working with structured and unstructured data.
Experience developing and deploying ML solutions in a cloud environment, especially AWS.
Ability to move from prototype to production, including model packaging, inference, and monitoring.
Excellent written and spoken English.
Master’s degree in Science or Engineering, such as Computer Science, Mathematics, Physics, or Statistics, or equivalent practical experience.
Experience with scikit-learn, PyTorch, TensorFlow, XGBoost, or similar modeling stacks (nice to have).
Familiarity with ML experiment tracking and reproducibility tools such as MLflow or Weights & Biases (nice to have).
Experience with SQL, data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark (nice to have).
Exposure to feature stores, embedding pipelines, or vector search for retrieval-based systems (nice to have).
Experience building HTTP/gRPC APIs or lightweight model inference services (nice to have).
Working knowledge of Docker, orchestration, and CI/CD such as GitLab CI (nice to have).
Experience in agile, remote, and async team environments (nice to have).
Publications, patents, Kaggle results, or open-source ML contributions (nice to have).

Benefits

Competitive compensation, including stock options.
Flexible hours.
Remote or home office options.
A calm, engineers-only office when on-site.
High trust and autonomy in how you reach goals.
Healthy meeting policy with protected focus time.
Meaningful internal product impact on developer and user experience.
Opportunities to participate in ML patent submissions and weekly research review meetings.

Interested in this position?

Apply directly on the company website

Apply Now

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