{"schemaVersion":"jobsearcher.job.v1","id":"887232c544e56e5b34b462cd","url":"https://jobsearcher.com/jobs/887232c544e56e5b34b462cd","canonicalUrl":"https://jobsearcher.com/jobs/887232c544e56e5b34b462cd","title":"Machine Learning Data Engineer (DataOps), Materra","description":"Software EngineeringMountain View, CA\nAbout the team:\nMaterra 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.\n\nAbout the Role\n\nWe 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.\n\nYour 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.\n\nKey Responsibilities\nArchitect 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.\nImplement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection to catch corrupt or mislabeled data early.\nStandardize and integrate third-party annotation workflows and remote labeling feeds into unified datasets ready for model training.\nDesign and maintain dataset versioning and storage systems to allow reproducible machine learning experiments and seamless data retrieval.\nCollaborate with machine learning engineers and operations teams to translate raw material, form factor, and sensor metadata into structured training features.\nRequirements\nEducation: Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.\nData Engineering & Architecture: 3+ years experience building scalable data pipelines, managing relational and non-relational databases, and unifying fragmented data storage systems.\nModern Python Proficiency: Expertise in Python and data manipulation libraries (e.g., Pandas, NumPy, or SQL).\nData Quality & DataOps: Practical experience implementing automated data validation, quality control frameworks, and dataset versioning practices.\nML Data Lifecycle Understanding: Hands-on experience structuring datasets specifically for machine learning workflows, including handling annotations, metadata tracking, and training set curation.\nPreferred Skills\nGoogle Cloud Ecosystem: Hands-on experience with Google Cloud platform tools (e.g., BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines).\nWorkflow Orchestration: Experience managing pipelines using Google Cloud Composer or equivalent orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster).\nMultimodal / Unstructured Data: Experience handling mixed data types, including image datasets, sensor metadata, and unstructured physical property records.\nAnnotation Platform Integration: Familiarity with data labeling platforms, human-in-the-loop workflows, or integrating third-party annotation APIs.\nValidation & Versioning Tooling: Exposure to data quality and ML versioning tools (e.g., Great Expectations, DVC, or TFX/Data Validation).\nThe 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.\n\nPlease note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.\nAn Equal Opportunity Workplace\nAt X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.\nIf you have a disability or special need that requires accommodation, please contact us at x-accommodation-request@x.team.","company":"Loon","rawCompany":"loon","city":"Mountain View","state":"HI","isRemote":false,"isActive":false,"createdAt":"2026-08-05T00:14:14.683Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Data Engineer (DataOps), Materra","description":"Software EngineeringMountain View, CA\nAbout the team:\nMaterra 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.\n\nAbout the Role\n\nWe 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.\n\nYour 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.\n\nKey Responsibilities\nArchitect 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.\nImplement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection to catch corrupt or mislabeled data early.\nStandardize and integrate third-party annotation workflows and remote labeling feeds into unified datasets ready for model training.\nDesign and maintain dataset versioning and storage systems to allow reproducible machine learning experiments and seamless data retrieval.\nCollaborate with machine learning engineers and operations teams to translate raw material, form factor, and sensor metadata into structured training features.\nRequirements\nEducation: Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.\nData Engineering & Architecture: 3+ years experience building scalable data pipelines, managing relational and non-relational databases, and unifying fragmented data storage systems.\nModern Python Proficiency: Expertise in Python and data manipulation libraries (e.g., Pandas, NumPy, or SQL).\nData Quality & DataOps: Practical experience implementing automated data validation, quality control frameworks, and dataset versioning practices.\nML Data Lifecycle Understanding: Hands-on experience structuring datasets specifically for machine learning workflows, including handling annotations, metadata tracking, and training set curation.\nPreferred Skills\nGoogle Cloud Ecosystem: Hands-on experience with Google Cloud platform tools (e.g., BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines).\nWorkflow Orchestration: Experience managing pipelines using Google Cloud Composer or equivalent orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster).\nMultimodal / Unstructured Data: Experience handling mixed data types, including image datasets, sensor metadata, and unstructured physical property records.\nAnnotation Platform Integration: Familiarity with data labeling platforms, human-in-the-loop workflows, or integrating third-party annotation APIs.\nValidation & Versioning Tooling: Exposure to data quality and ML versioning tools (e.g., Great Expectations, DVC, or TFX/Data Validation).\nThe 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.\n\nPlease note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.\nAn Equal Opportunity Workplace\nAt X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.\nIf you have a disability or special need that requires accommodation, please contact us at x-accommodation-request@x.team.","datePosted":"2026-08-05T00:14:14.683Z","dateModified":"2026-08-05T00:14:14.683Z","hiringOrganization":{"@type":"Organization","name":"Loon","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"HI","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"887232c544e56e5b34b462cd"},"url":"https://jobsearcher.com/jobs/887232c544e56e5b34b462cd"}}