Senior Machine Learning Engineer – Implementation & Scale
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Die Stelle
Design, train, and deploy state-of-the-art ML models (Deep Learning, Transformers, Gradient Boosting) tailored to proprietary datasets.
Build and sustain end-to-end data pipelines and training workflows for reproducible, scalable model development.
Profile and optimize latency and throughput for production inference while preserving performance.
Conduct deep EDA to uncover biases, signal-to-noise, and opportunities for feature engineering within unique data silos.
Collaborate with Data Engineers and Backend Engineers to streamline ingestion and expose model APIs to user-facing features.
Adopt a pragmatic, ownership-driven approach to monitor models in production, address drift, and mentor junior engineers.
Build and sustain end-to-end data pipelines and training workflows for reproducible, scalable model development.
Profile and optimize latency and throughput for production inference while preserving performance.
Conduct deep EDA to uncover biases, signal-to-noise, and opportunities for feature engineering within unique data silos.
Collaborate with Data Engineers and Backend Engineers to streamline ingestion and expose model APIs to user-facing features.
Adopt a pragmatic, ownership-driven approach to monitor models in production, address drift, and mentor junior engineers.
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