Senior Data Processing Engineer

Berlin, Germany | Hybrid - 3 days onsite per week

Join a highly technical AI research and engineering organisation building the data foundations for next-generation AI models. As a Senior ML Data Processing Engineer, you’ll develop and optimise large-scale data pipelines that transform raw, web-scale data into high-quality training datasets.

You’ll work at the intersection of data engineering, machine learning and NLP, improving data quality through automated filtering, deduplication, ML-based evaluation and privacy controls. Working closely with researchers and engineers, you’ll help ensure training datasets are reliable, reproducible and suitable for advanced model development.

Key Responsibilities

• Build and optimise distributed pipelines for processing large-scale, unstructured datasets for ML training.
• Implement data curation and quality controls, including deduplication, filtering, PII removal, toxicity detection and metadata extraction.
• Develop automated data-quality systems using ML classifiers, heuristics and LLM-based evaluators.
• Establish dataset versioning, lineage and reproducibility, including mechanisms to detect data leakage and evaluation contamination.
• Monitor pipeline performance and data quality, identifying bottlenecks, regressions and opportunities to improve efficiency.
• Collaborate with Research, Engineering and Governance teams on dataset requirements, privacy, licensing and compliance.

Required Experience & Skills

• PhD in a relevant technical discipline.
• At least 3 years of relevant experience in data processing, ML engineering, NLP or a closely related field.
• Experience working with massive, unstructured text datasets and distributed processing frameworks such as Apache Spark, Ray or Flink.
• Strong Python skills and experience building production-grade data-processing pipelines.
• Practical experience with data curation, deduplication, filtering, dataset quality or large-scale dataset construction.
• Understanding of data privacy, PII removal, content filtering, evaluation contamination and dataset reproducibility.
• Experience with workflow orchestration tools such as Airflow, Prefect or Dagster.
• Experience with LLM training or fine-tuning, LLM inference tools such as vLLM or SGLang, Docker/Kubernetes, web-scale data acquisition, data licensing or open-source NLP projects.

By applying to this role you understand that we may collect your personal data and store and process it on our systems. For more information please see our Privacy Notice (