Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4349085675&f_AL=true&f_TPR=r10000&f_WT=2&keywords=software%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&start=100 Job Description: Senior Machine Learning Engineer - MlOps  United States · 2 hours ago · 73 applicants Promoted by hirer · No response insights available yet Remote Matches your job preferences, workplace type is Remote. Full-time Easy Apply Save Save Senior Machine Learning Engineer - MlOps  at Established Search Senior Machine Learning Engineer - MlOps Established Search · United States (Remote) Easy Apply Save Save Senior Machine Learning Engineer - MlOps  at Established Search Show more options Your profile is missing required qualifications Show match details Help me update my profile BETA Is this information helpful? Get personalized tips to stand out to hirers Practice mock interviews personalized to every role and get custom feedback Try Premium for PKR0 Meet the hiring team Klaudia Topa 3rd Principal Global Recruiter within Medical Imaging and AI Job poster Message About the job Senior Machine Learning Engineer (ML Ops) – Medical Imaging AI About the Role We are seeking a Senior Machine Learning Engineer (ML Ops) to support the development, deployment, and maintenance of AI-powered medical imaging technologies. This role sits at the intersection of machine learning, medical imaging, and production-scale software engineering. You will help build robust ML pipelines, deploy deep learning models into clinical environments, and ensure the reliability and compliance of AI/ML Software as a Medical Device (SaMD). Experience with CT/MRI imaging is highly valuable. Key Responsibilities ML Pipeline Development & Deep Learning Operations Build and maintain ML infrastructure supporting AI-powered CT/MRI image enhancement and analysis. Develop and operationalize deep learning pipelines, including data ingestion, preprocessing, training, validation, and deployment. Write production-grade code to set up, optimize, and maintain deep learning model architectures. Feed imaging data into training workflows and evaluate performance using separate validation and test datasets. Improve the efficiency of deep learning algorithms to run effectively on standard hospital scanners and clinical hardware. Software Engineering & Systems Deployment Deploy, monitor, and maintain software systems across cloud platforms and on-premise clinical sites. Build and support CI/CD pipelines for model release, testing, and continuous deployment. Develop tools for experiment tracking, model versioning, and reproducible research workflows. Run simulations and performance analyses (manual or automated) to evaluate system behavior under varying conditions. Monitor system operations to detect issues early and support troubleshooting. Quality, Compliance & Documentation Participate in Quality Management System (QMS) processes supporting AI/ML SaMD products. Ensure compliance with regulatory frameworks including ISO 13485, ISO 14971, IEC 62304, and IEC 62366. Maintain detailed logs of code and model changes in accordance with QMS procedures. Conceive, document, and carry out software design and testing plans. Support data protection activities including audits, investigations, and required training. Collaboration, Innovation & Research Support Work closely with research teams to transition next-generation AI imaging models from experimentation to production. Support publication of new technologies and contribute to patent submissions for innovative ML solutions. Identify system, hardware, or software components needed to meet user and customer requirements. Qualifications Required Master’s degree in Computer Science, Engineering, or related field. 5+ years of software engineering experience, including work as a full-stack developer and/or ML Ops engineer. Strong programming skills in Python, C, and/or C++. Understanding of medical imaging principles (CT, MRI) and/or deep learning pipelines for medical images. Working knowledge of QMS regulations and standards: ISO 13485, ISO 14971, IEC 62304, IEC 62366.