You have a deep understanding of the ML lifecycle, including dataset preparation, distributed training, experiment management, validation, and reproducibility. You will own and improve the end-to-end ML lifecycle—from data processing, selection, and annotation through distributed model training, experiment tracking and validation. * Design, implement, and operate scalable ML and data pipelines covering ingestion, dataset creation, training, and model validation. * Build a reliable ML platform for reproducible experimentation and training across local, on-premises, and cloud GPU infrastructure. * Establish dataset and model versioning, metadata management, quality checks, and traceability from raw sensor data to validated artifacts. * Build and optimize distributed
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