Machine Learning Data Engineer, Replica Pipelines
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El puesto
Own data ingestion: Build reliable pipelines to normalize and validate customer and synthetic data.
Define data standards: Create schemas, validation checks, and quality metrics for datasets used in ML.
Build curation tooling: Implement tools for dataset filtering, versioning, and annotation support.
Enable ML workflows: Generate high-quality data feeds for training and evaluation across models.
Collaborate closely with ML engineers, applying 3D and computer vision fundamentals to ensure data is ML-ready.
Operate in a modern cloud-based data platform with emphasis on scalability, reproducibility, and dataset governance.
Define data standards: Create schemas, validation checks, and quality metrics for datasets used in ML.
Build curation tooling: Implement tools for dataset filtering, versioning, and annotation support.
Enable ML workflows: Generate high-quality data feeds for training and evaluation across models.
Collaborate closely with ML engineers, applying 3D and computer vision fundamentals to ensure data is ML-ready.
Operate in a modern cloud-based data platform with emphasis on scalability, reproducibility, and dataset governance.
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