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: Position Overview The Sr.AI/MLEngineerguidesthe architecture, technical roadmap, and hands-on implementation of thelife sciencesAI platformproviding the foundations for agentic solutions across the science and medical offices.This role helps shape the organization’s AI strategy and evaluates emerging state-of-the-art technologies for practical implementation within the platform.This role serves both technical consumers intheBioinformatics,Biostatistics and Data Science group,and non-technical consumers across Clinical Affairs, Regulatory Affairs, and Medical Operationswithin a setting of advanced cancer screening and precision oncology.The work combines pragmatic experimentation with a clear path beyond prototypes, producing solutions that are evaluated, validated, governed, supported, and reusable.This role defines the engineering standards, controls, and validation framework under whichgenerativeAI solutions are developed andpublished, andprovides technical leadership through mentorship and architecture review. Essential Duties Include, but are not limited to, the following: - Guidethe architecture, technical roadmap, and strategic evolution of thelife sciencesgenerativeAI platform, including its runtime, gateway, memory, identity, observability, and evaluation capabilities, to accelerate and improve scientific and medical processes and outcomes. - Design and implement shared platform services, reusable components, and supported solution patterns that generalize recurring needs and reduce reliance on function-specific point solutions. - Integrate agent frameworks and interoperability protocols with enterprise data sources, document repositories, and scientific systems using secure, supported platform patterns. - Partner with stakeholders and AI/ML engineers across science and medical functions to translate operational workflows, SOPs, and business needs into scalable, supported AI solutions. - Driveplatformadoption bydefining and applying evaluation criteria that demonstrate solution quality, reliability, efficiency, and business value. - Apply Agile practices to iteratively develop, evaluate, and mature promising concepts into validated, reusable solutions. - Provide mentorship and coaching to more junior level team members. - Act as resource and subject matter expert in core team and/or cross-functional meetings. - Communicate difficult, sensitive, and complex information clearly to technical and non-technical stakeholders. - Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork. - Support and comply with the company’s Quality Management System policies and procedures. - Maintain regular and reliable attendance and availability during the designated work schedule. - Act with an inclusion mindset and model these behaviors for the organization. - Ability to work on a mobile device, tablet, or in front of a computer screen and/or perform typing for approximately 85% of a typical working day. - Ability to travel 5% of working time away from work location, may include overnight/weekend travel. Minimum Qualifications - Ph. Din Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties, or master’s degree in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties plus 4 years of experience in lieu of a Ph.D. - 3+ years of experience in statistics, computational biology, applied mathematics, or related quantitative field as outlined in the essential duties. - 3+ years of experience with artificial intelligence and machine learning algorithms. - Demonstrated knowledge and experience with advanced AI concepts, such as artificial neural networks, deep learning, and reinforcement learning. - Demonstrated knowledge and experience using artificial intelligence and machine learning techniques within one or more of the following fields: natural language processing, image processing and computer vision, image and pattern recognition. - Demonstrated knowledge and experience with large language models for generative AI and associated concepts, such as transformer architecture and retrieval augmented generation. - Strong programming ability with demonstrated experience in Python and one or more associated machine learning frameworks, such as TensorFlow,PyTorch, orSKLearn. - Knowledge of and experience working with open-source AI models. - Demonstrated ability to perform the essential duties of the position with or without accommodation. - Authorization to work in the United States without sponsorship. Preferred Qualifications - 2+ years of life sciences industry experience working with biological data. - 2+ years of industry experience in molecular diagnostics, preferably cancer diagnostics. - Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data. - Basic knowledge of ML-Ops and processes for managing the versioning and deployment of machine learning models. - Scientific understanding of cancer biology The base pay for this position is $78,000.00 – $156,000.00In specific locations, the pay range may vary from the range posted. JOB FAMILY: Product DevelopmentDIVISION: ONCO Cancer DiagnosticsLOCATION: United States of America : RemoteADDITIONAL LOCATIONS: WORK SHIFT: StandardTRAVEL: Yes, 5 % of the TimeMEDICAL SURVEILLANCE: Not ApplicableSIGNIFICANT 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: is the Law link - Espanol: