Staff, Machine Learning Engineer
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Die Stelle
Line 1: Lead the design and implementation of scalable, production-grade ML solutions addressing business-critical needs.
Line 2: Translate ambiguous problem statements into clear technical requirements, architectural designs, and success metrics.
Line 3: Oversee end-to-end ML lifecycle including data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement with MLOps practices.
Line 4: Build and maintain reusable infrastructure components (feature stores, model runtimes, SDKs) and manage large-scale ML operations across cloud environments with containers and Kubernetes.
Line 5: Provide hands-on technical leadership, set engineering standards, and mentor engineers to ensure quality and operational excellence.
Line 6: Design experimentation pipelines (A/B testing, AutoML, NAS) and drive continuous improvement while applying responsible AI practices.
Line 2: Translate ambiguous problem statements into clear technical requirements, architectural designs, and success metrics.
Line 3: Oversee end-to-end ML lifecycle including data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement with MLOps practices.
Line 4: Build and maintain reusable infrastructure components (feature stores, model runtimes, SDKs) and manage large-scale ML operations across cloud environments with containers and Kubernetes.
Line 5: Provide hands-on technical leadership, set engineering standards, and mentor engineers to ensure quality and operational excellence.
Line 6: Design experimentation pipelines (A/B testing, AutoML, NAS) and drive continuous improvement while applying responsible AI practices.
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