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ML Ops Engineer

1 month, 4 weeks ago
Czech Republic
Full-time
Mid Level
Machine Learning Engineer
Software Development
Agile Apache Airflow Apache Spark AWS CI/CD dbt Deep Learning Docker Feature Engineering 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 compare approaches.
  • Bring models and ML workflows into production with reliable, reproducible processes.
  • Perform feature engineering, dataset construction, labeling checks, and train/validation/test evaluation.
  • Analyze model performance using clear metrics, error analysis, and tradeoff assessment.
  • Develop ML solutions that address real business or product problems.
  • Package models, build inference paths, monitor performance, and iterate after launch.
  • Collaborate closely with engineers, product partners, and other data scientists.
  • Explain methods, results, and limitations to both technical and non-technical audiences.
  • Own work end to end from problem framing through experimentation, implementation, and rollout.

Requirements

  • 3+ years of software or applied ML experience.
  • Strong foundation in machine learning, statistics, and experiment design.
  • Proficiency in Python and ability to write clean, modular, testable code.
  • Experience building models for real business or product problems.
  • Comfort with structured and unstructured data and strong train/validation/test discipline.
  • Experience developing and deploying ML solutions in a cloud environment, especially AWS.
  • 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.
  • Familiarity with ML experiment tracking and reproducibility tools such as MLflow or 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 such as GitLab CI.
  • Experience working in agile, remote, and async team environments.
  • Publications, patents, Kaggle/competition results, or open-source ML contributions.

Benefits

  • Competitive compensation, including stock options.
  • Flexible hours.
  • Remote and home office options.
  • A calm, engineers-only office when on-site.
  • Healthy meeting policy with protected focus time.
  • High trust and autonomy in how work is completed.
  • Meaningful internal product impact on developer and user experience.
  • Collaboration on ML patent submissions and weekly ML/research paper review meetings.

Interested in this position?

Apply directly on the company website

Apply Now

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