Staff, Machine Learning Engineer
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Die Stelle
Lead the design and implementation of scalable, production-grade ML solutions that address business-critical needs.
Translate complex or ambiguous problems into clear technical requirements, architectures, and success metrics.
Build reusable ML infrastructure (feature stores, model runtimes, SDKs) and manage cloud operations with Kubernetes.
Oversee end-to-end ML lifecycle: data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement with MLOps.
Provide hands-on mentorship, set engineering standards, and foster collaboration and technical depth across teams.
Design and manage experimentation pipelines, AutoML options, NLP tooling, and multimodal architectures to accelerate delivery while ensuring responsible AI.
Translate complex or ambiguous problems into clear technical requirements, architectures, and success metrics.
Build reusable ML infrastructure (feature stores, model runtimes, SDKs) and manage cloud operations with Kubernetes.
Oversee end-to-end ML lifecycle: data sourcing, feature engineering, model training, deployment, monitoring, and continuous improvement with MLOps.
Provide hands-on mentorship, set engineering standards, and foster collaboration and technical depth across teams.
Design and manage experimentation pipelines, AutoML options, NLP tooling, and multimodal architectures to accelerate delivery while ensuring responsible AI.
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