{"schemaVersion":"jobsearcher.job.v1","id":"281170952fb74e25d8f9502e","url":"https://jobsearcher.com/jobs/281170952fb74e25d8f9502e","canonicalUrl":"https://jobsearcher.com/jobs/281170952fb74e25d8f9502e","title":"Senior MLOps, Data Systems Engineer","description":"Job Description: ML Pipeline & Data Systems Development: Design, build, and maintain scalable pipelines that span data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability across the full ML lifecycle.\r\nData & Annotation Pipeline Integration: Build and integrate annotation workflows with upstream data ingestion and training systems, enabling efficient task creation, labeling, QA, and dataset updates that directly support model iteration.\r\nData-Centric Iteration: Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops.\r\nExperimentation & Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.\r\nCI/CD for Machine Learning: Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines.\r\nModel Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.\r\nMonitoring & Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.\r\nCompute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.\r\nCross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.\r\nEnd-to-End Contribution: Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.\r\nRequirements: 5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.\r\nStrong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.\r\nExperience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.\r\nExperience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.\r\nStrong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.\r\nExperience with experiment tracking and model lifecycle management.\r\nFamiliarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.\r\nExperience with containerization (Docker) and workflow orchestration systems.\r\nExperience with cloud-based ML environments (e.g., AWS) and distributed training workflows.\r\nStrong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.\r\nStrong problem-solving and debugging skills, particularly in complex, multi-stage systems.\r\nBachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).\r\nBenefits: Comprehensive Health & Wellness: A choice of medical, dental, and vision plans. We also provide company-paid life and disability insurance and company-funded mental health benefits.\r\nFinancial & Retirement Planning: 401(k) plan with both pre-tax and Roth options, and access to a Health Savings Account (HSA) with a monthly company contribution.\r\nFamily & Fertility Support: Paid parental leave for birthing and non-birthing parents, plus fertility and family-forming benefits.\r\nPaid Time Off: Unlimited vacation, paid leaves, and 10 company holidays.\r\nUnique Lime Perks: Complimentary use of Lime vehicles in participating cities, a monthly phone allowance, dedicated learning and development days, and access to perks including One Medical, Wellhub, and Headspace.","company":"Remote Rocketship","rawCompany":"remoterocketship","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-05-14T03:49:26.555Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior MLOps, Data Systems Engineer","description":"Job Description: ML Pipeline & Data Systems Development: Design, build, and maintain scalable pipelines that span data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability across the full ML lifecycle.\r\nData & Annotation Pipeline Integration: Build and integrate annotation workflows with upstream data ingestion and training systems, enabling efficient task creation, labeling, QA, and dataset updates that directly support model iteration.\r\nData-Centric Iteration: Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops.\r\nExperimentation & Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.\r\nCI/CD for Machine Learning: Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines.\r\nModel Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.\r\nMonitoring & Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.\r\nCompute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.\r\nCross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.\r\nEnd-to-End Contribution: Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.\r\nRequirements: 5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.\r\nStrong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.\r\nExperience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.\r\nExperience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.\r\nStrong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.\r\nExperience with experiment tracking and model lifecycle management.\r\nFamiliarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.\r\nExperience with containerization (Docker) and workflow orchestration systems.\r\nExperience with cloud-based ML environments (e.g., AWS) and distributed training workflows.\r\nStrong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.\r\nStrong problem-solving and debugging skills, particularly in complex, multi-stage systems.\r\nBachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).\r\nBenefits: Comprehensive Health & Wellness: A choice of medical, dental, and vision plans. We also provide company-paid life and disability insurance and company-funded mental health benefits.\r\nFinancial & Retirement Planning: 401(k) plan with both pre-tax and Roth options, and access to a Health Savings Account (HSA) with a monthly company contribution.\r\nFamily & Fertility Support: Paid parental leave for birthing and non-birthing parents, plus fertility and family-forming benefits.\r\nPaid Time Off: Unlimited vacation, paid leaves, and 10 company holidays.\r\nUnique Lime Perks: Complimentary use of Lime vehicles in participating cities, a monthly phone allowance, dedicated learning and development days, and access to perks including One Medical, Wellhub, and Headspace.","datePosted":"2026-05-14T03:49:26.555Z","dateModified":"2026-05-14T03:49:26.555Z","hiringOrganization":{"@type":"Organization","name":"Remote Rocketship","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"281170952fb74e25d8f9502e"},"url":"https://jobsearcher.com/jobs/281170952fb74e25d8f9502e"}}