Staff AI/ML Platform Engineer

Abbott 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 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: At Lingo, we’re building a groundbreaking health platform that combines continuous biosensor data, real-time analytics, and personalized insights to help people live fuller, longer, and healthier lives. Our systems ingest millions of sensor readings daily, powering experiences for consumers and partners worldwide, with the reliability and scalability of cloud-native, enterprise-grade platforms. Working at Abbott At Abbott, you can do work that matters, grow, and learn, care for yourself and your 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 year. An excellent retirement savings plan with a high employer contribution. Tuition 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 worldwide 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 the best place to work for diversity, working mothers, female executives, and scientists. THE OPPORTUNITY This Staff AI/ML Platform Engineer position can work out of Alameda, CA location. As Staff AI/ML Platform Engineer, you will design and build the platform that takes machine learning models from experiment to production, safely and repeatably, across cloud and device. You will connect our existing data lake and ETL pipelines to ML infrastructure and enable both the AI engineering and data science teams to ship with confidence. You will work closely with data engineering, DevOps and SRE, data science, security, quality, and regulatory teams, so a high level of technical judgement and professionalism is required. What You'll Work On Define the architecture and roadmap for the AI platform, feature store, training pipelines, model registry, deployment, and serving. Partner with the data engineering team to define the boundary between curated data and ML features and ensure lineage from raw sensor data to model output. Enable data scientists to self-serve training and deployment through templates, tooling, and documentation. Extend the existing CI/CD pipelines with model-specific stages, including automated testing, validation gates, and approval workflows aligned with design controls. Implement model monitoring for performance, data drift, and sensor data quality on the shared observability stack, with alerting and rollback. Build the pipeline for packaging and delivering models to mobile and wearable devices, in partnership with the on-device AI/ML engineer. Support deployment and monitoring of LLM and agentic services, including prompt and model versioning and evaluation runs. Own training and inference cost and ML tooling decisions; partner with DevOps and SRE on infrastructure capacity, scalability, and reliability. Ensure the ML platform meets security, privacy, and compliance requirements (HIPAA, GDPR) and produces audit-ready records. undefined Qualifications: Bachelor’s degree in Engineering, Mathematics, Computer Science, or a related field. Minimum 8 years of software or ML engineering experience, including 3+ years deploying ML models on mobile or embedded devices. Hands-on experience with Azure ML SDK, Azure Data Factory, Databricks and ML tooling such as MLflow, Kubeflow. Strong proficiency in Python, working knowledge of containers (Docker, Kubernetes), infrastructure as code, and at least one major cloud platform. Experience partnering with DevOps and SRE teams on shared infrastructure. Experience with data lakes, streaming or time-series data, and feature stores. Proven experience implementing model monitoring, drift detection, and automated retraining. Preferred Qualifications Ph.D. or master’s degree in computer science, Machine Learning or a related field. Experience in a regulated industry, ideally medical devices or SaMD, including validated software environments. Familiarity with IEC 62304, ISO 13485, FDA guidance on AI/ML-enabled devices, and HIPAA. Experience deploying models to mobile or edge devices. Experience supporting LLM operations, such as prompt management, evaluation pipelines, and inference cost control. Experience with high-volume IoT or wearable sensor data. undefined Learn more about our health and wellness benefits, which provide the security to help you and your family live full lives: www.abbottbenefits.com 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 www.abbott.com , on Facebook at www.facebook.com/Abbott and on Twitter @AbbottNews and @AbbottGlobal #software The base pay for this position is $114,000.00 – $228,000.00 In specific locations, the pay range may vary from the range posted. JOB FAMILY: Product Development DIVISION: LNGO Lingo LOCATION: United States > Alameda : 2901 Harbor Bay Parkway ADDITIONAL LOCATIONS: WORK SHIFT: Standard TRAVEL: No MEDICAL SURVEILLANCE: Not Applicable SIGNIFICANT WORK ACTIVITIES: Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday) Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans. EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf