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ML Ops Engineer

1 month, 4 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

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
1K-5K
Founded 2007
View All Jobs 21

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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