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.
- Implement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection to catch corrupt or mislabeled data early.
- Standardize and integrate third-party annotation workflows and remote labeling feeds into unified datasets ready for model training.
- Design and maintain dataset versioning and storage systems to allow reproducible machine learning experiments and seamless data retrieval.
- Collaborate with machine learning engineers and operations teams to translate raw material, form factor, and sensor metadata into structured training features.
Requirements
- Education: Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
- Data Engineering & Architecture: 3+ years experience building scalable data pipelines, managing relational and non-relational databases, and unifying fragmented data storage systems.
- Modern Python Proficiency: Expertise in Python and data manipulation libraries (e.g., Pandas, NumPy, or SQL).
- Data Quality & DataOps: Practical experience implementing automated data validation, quality control frameworks, and dataset versioning practices.
- ML Data Lifecycle Understanding: Hands-on experience structuring datasets specifically for machine learning workflows, including handling annotations, metadata tracking, and training set curation.
Preferred Skills
- Google Cloud Ecosystem: Hands-on experience with Google Cloud platform tools (e.g., BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines).
- Workflow Orchestration: Experience managing pipelines using Google Cloud Composer or equivalent orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster).
- Multimodal / Unstructured Data: Experience handling mixed data types, including image datasets, sensor metadata, and unstructured physical property records.
- Annotation Platform Integration: Familiarity with data labeling platforms, human-in-the-loop workflows, or integrating third-party annotation APIs.
- Validation & Versioning Tooling: Exposure to data quality and ML versioning tools (e.g., Great Expectations, DVC, or TFX/Data Validation).
The US base salary range for this full-time position is $166,000 - $244,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
