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.
Translate complex problems into clear technical requirements, architectures, and success metrics; select and integrate ML frameworks and languages to meet performance and explainability standards.
Oversee the full ML lifecycle—data sourcing, feature engineering, model training, deployment, monitoring, and continual improvement.
Apply MLOps best practices (CI/CD for ML, automated training pipelines, model versioning, telemetry) and establish robust evaluation for performance, data quality, and fairness.
Build and maintain reusable ML infrastructure components (feature stores, model runtimes, SDKs) and manage operations across cloud environments with containerization and Kubernetes.
Provide technical leadership and mentorship, drive experimentation and innovation, and uphold high standards for quality, collaboration, and accountability.
Translate complex problems into clear technical requirements, architectures, and success metrics; select and integrate ML frameworks and languages to meet performance and explainability standards.
Oversee the full ML lifecycle—data sourcing, feature engineering, model training, deployment, monitoring, and continual improvement.
Apply MLOps best practices (CI/CD for ML, automated training pipelines, model versioning, telemetry) and establish robust evaluation for performance, data quality, and fairness.
Build and maintain reusable ML infrastructure components (feature stores, model runtimes, SDKs) and manage operations across cloud environments with containerization and Kubernetes.
Provide technical leadership and mentorship, drive experimentation and innovation, and uphold high standards for quality, collaboration, and accountability.
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