greenhouse
Machine Learning Data Engineer (DataOps), Materra
X
USA
Full-time
Mid Level
Onsite
USD 166000-244000 yearly
Discovered 1 weeks ago
Free
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We are looking for a Machine Learning Data Engineer (DataOps) to build and unify the data infrastructure that powers our model training pipelines.
In this role, you will lead the effort to consolidate fragmented data sources into a cohesive, high-quality data foundation.
Your primary focus will be designing automated ingestion pipelines, establishing data quality validation frameworks, and managing dataset versioning to support our machine learning training loops.
You will bridge the gap between operations, remote annotation teams, and machine learning engineers to ensure our models are trained on reliable, well-structured data.
Full Job Posting
About the team:
Materra is on a mission to radically reduce global waste and move to a true circular economy.
The team has developed technology that identifies waste material at the molecular level—starting with plastics.
Materra works with industry partners to improve the way recycling centers process plastics using AI and robotics, to make recycling more affordable and scalable.
About The Role
We are looking for a Machine Learning Data Engineer (DataOps) to build and unify the data infrastructure that powers our model training pipelines.
In this role, you will lead the effort to consolidate fragmented data sources into a cohesive, high-quality data foundation.
Your primary focus will be designing automated ingestion pipelines, establishing data quality validation frameworks, and managing dataset versioning to support our machine learning training loops.
You will bridge the gap between operations, remote annotation teams, and machine learning engineers to ensure our models are trained on reliable, well-structured data.
Key Responsibilities
- Architect and build automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data pipelines to aggregate, clean, and harmonize data from disparate sources, databases, and operational ingestion flows.
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