Staff, Machine Learning Engineer
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Die Stelle
Lead the design and delivery of scalable, production-grade ML solutions that address business-critical needs, ensuring reliability and explainability.
Translate ambiguous problems into clear technical requirements, architectures, and measurable success criteria.
Select and integrate ML frameworks and languages (Python, SQL, Java, C++, R; Scikit-learn, TensorFlow, PyTorch, XGBoost, Spark ML) to meet performance and maintainability targets.
Oversee the full ML lifecycle—from data sourcing and feature engineering to model training, deployment, monitoring, and continuous improvement—applying robust MLOps practices.
Build reusable platform components (feature stores, model runtimes, SDKs) and manage large-scale ML operations across cloud environments with Kubernetes and containerization; implement observability to detect drift.
Provide technical leadership and mentorship, drive experimentation, and foster collaboration, integrity, and responsible AI throughout the team.
Translate ambiguous problems into clear technical requirements, architectures, and measurable success criteria.
Select and integrate ML frameworks and languages (Python, SQL, Java, C++, R; Scikit-learn, TensorFlow, PyTorch, XGBoost, Spark ML) to meet performance and maintainability targets.
Oversee the full ML lifecycle—from data sourcing and feature engineering to model training, deployment, monitoring, and continuous improvement—applying robust MLOps practices.
Build reusable platform components (feature stores, model runtimes, SDKs) and manage large-scale ML operations across cloud environments with Kubernetes and containerization; implement observability to detect drift.
Provide technical leadership and mentorship, drive experimentation, and foster collaboration, integrity, and responsible AI throughout the team.
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