Machine Learning Engineer
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Die Stelle
Own and optimize mid-level ML lifecycle stages, including data ingestion, preprocessing, distributed training, evaluation, deployment, and monitoring.
Design and optimize scalable data pipelines for large datasets across distributed compute resources.
Enhance multi-GPU and multi-node training workflows to maximize throughput.
Automate end-to-end ML processes with experiment tracking, model versioning, and CI/CD for ML workflows.
Identify bottlenecks in data processing, training, and deployment to deliver reliable, production-ready systems.
Provide robust distributed infrastructure that accelerates prototyping and enables researchers to innovate efficiently.
Design and optimize scalable data pipelines for large datasets across distributed compute resources.
Enhance multi-GPU and multi-node training workflows to maximize throughput.
Automate end-to-end ML processes with experiment tracking, model versioning, and CI/CD for ML workflows.
Identify bottlenecks in data processing, training, and deployment to deliver reliable, production-ready systems.
Provide robust distributed infrastructure that accelerates prototyping and enables researchers to innovate efficiently.
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