Principal AI/ML Engineer

Job ID: 31142847Company: Abbott LaboratoriesLocation: United States - California - Santa ClaraJob Type: Full timeCategory: Research & DevelopmentIndustry: HealthcarePosted Date: 2026-06-03Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.Our medical devices help more than 10,000 people have healthier hearts, improve quality of life for thousands of people living with chronic pain and movement disorders, and liberate more than 500,000 people with diabetes from routine fingersticks.Working at AbbottAt Abbott, you can do work that matters, grow, and learn, care for yourself and family, be your true self and live a full life. You’ll also have access to:Career development with an international company where you can grow the career you dream of.Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar yearAn excellent retirement savings plan with high employer contributionTuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.THE OPPORTUNITYThis Principal AI/ML Engineer position can work out of our Santa Clara, CA location.The Principal ML Ops Engineer will lead the technical execution of Abbott’s Medical Devices Digital (MDD) AI initiatives, bridging advanced AI technology development with scalable engineering solutions. You will be vital in designing, developing and maintaining a robust AI platform, establishing production-grade MLOps capabilities, and collaborating closely with data scientists, infrastructure specialists, and algorithm teams to ensure effective AI solution deployments. What You’ll Work OnLead end-to-end ML solutions development and delivery, including data ingestion, annotation, feature engineering, training, validation, deployment, and monitoring. Architect a highly available, secure, scalable cloud/on-prem hybrid ML infrastructure. Engage directly with ML scientists and act as the team’s bridge/glue between science and engineering. Implement robust CI/CD workflows for ML models, including testing, rollout, rollback strategies, and compliance governance. Ensure strict compliance with regulatory and privacy standards such as HIPAA, GDPR, and Software as a Medical Device (SaMD) guidelines. Drive alignment and adoption of architecture strategy with business leaders. Mentor and guide ML engineers and SW engineers, establish coding standards, and conduct detailed design and architectural reviews. QualificationsBachelors Degree in Computer Science, Engineering Mathematics, or related field. Minimum 10+ years of experience, Master’s Degree with 7+ years of related experience, or Ph.D. with 5+ years of related experience. Deep experience building ML infrastructure for experiment tracking, model training, data versioning, annotation tools, model serving, monitoring and observability. Experience with GenAI and Agentic AI development infrastructure including grounding, RAG, MCP and prompt engineering. ​Experience developing AI products on cloud computing platforms (e.g., AWS, Azure, Google Cloud), containerization technologies (e.g., Docker, Kubernetes), pipeline orchestration tools (e.g. Airflow), IaC (e,g., Terraform). Proficient in tools like Azure ML SDK, Azure Data Factory, Databricks, Spark, or related technologies. Strong understanding of database technologies (e.g., SQL, NoSQL) and data modeling principles. Significant experience working with agile software development methods, such as scrum and Kanban and working within CI/CD pipelines. Experience incorporating Security by Design principles into AI product development. Experience of senior executive/leadership engagement. Exposure to various aspects of architecture practices and frameworks: business, application, data, security, infrastructure and governance. Experience with microservices architecture and distributed systems.Experience in reviewing and selecting Technical and Applications Architectures solutions. Certifications and specializations in AI/ML, LLMs and Cloud platforms. Excellent oral, written and presentation communication skills. Team leadership experience and demonstrated mentorship capabilities. Preferred Experience working in an FDA-regulated business (e.g. validated software related to medical, pharmaceutical, or life sciences products).Experience with FDA 510(k) submissions and clinical-grade ML product development.Solid understanding of the design thinking process, as well as a passion and know-how for influencing design strategy.Publications, patents, or notable contributions to open-source projects.Background in signal processing, computer vision, or multimodal learning.Familiarity with data security best practices, data anonymization, synthetic data generation, and federated learning.#softwareApply NowLearn more about our health and wellness benefits, which provide the security to help you and your family live full lives: Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.Connect with us at , on Facebook at and on Twitter @AbbottNews and @AbbottGlobalThe base pay for this position is $148,700.00 – $297,300.00. In specific locations, the pay range may vary from the range posted.Skillsmachine learning engineeringcloud computing platformsmlopsdata engineeringci/cd pipelinesmodel deploymentmodel monitoringfeature engineeringdata annotationmodel validationmodel servingdatabase technologiessqlnosqldatabricksazure ml sdkazure data factorysparkdockerkubernetesairflowterraformagile software developmentscrumkanbansecurity by designmicroservices architecturedistributed systemstechnical architecture reviewmentorship