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 115,000 colleagues serve people in more than 160 countries.

• Design, develop and deploy machine learning and deep learning models for clinical applications including image segmentation, feature detection, classification and quantitative analysis of OCT images.
• Lead the development of advanced semantic segmentation algorithms for OCT-based vascular imaging applications.
• Own end-to-end ML lifecycle, including data preparation, annotation strategies, model development, training, validation, performance optimization, and deployment.
• Collaborate with clinical and V&V teams to define clinically meaningful outputs, evaluation metrics, and validation methodologies.
• Develop algorithms capable of robust performance across diverse patient populations, imaging conditions, and clinical use cases.
• Optimize models for real-time or near-real-time inference in embedded or product environments.
• Evaluate and integrate state-of-the-art machine learning approaches, including foundation models, self-supervised learning, and generative AI techniques where appropriate.
• Contribute to software architecture, code reviews, and engineering best practices to ensure scalable and maintainable AI solutions.
• Generate technical documentation supporting design controls, verification, validation, risk management, and regulatory submissions.

• Master’s or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field with a focus on AI/ML.
• 7+ years of industry experience developing machine learning, deep learning, or computer vision solutions, or 4+ years of relevant industry experience with a PhD.
• Strong expertise in deep learning architectures and modern machine learning approaches for image analysis, including convolutional neural networks and vision transformers
• Proven experience developing semantic segmentation, object detection, classification, and image analysis algorithms.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch, TensorFlow, or equivalent.
• Experience designing experiments, evaluating model performance, and applying statistical methods for model validation.
• Demonstrated ability to translate research concepts into prototype and production-quality software solutions.
• Excellent communication and cross-functional communication skills.

• Experience with OCT, IVUS, CT, MRI or other medical imaging modalities, including multi-modal image registration techniques.
• Understanding of OCT imaging principles, image formation physics, and common imaging artifacts.
• Experience developing AI/ML solutions for 3D volumetric, temporal, longitudinal or sequential imaging datasets.
• Experience with foundation models, generative AI, self-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging.
• Expertise in model deployment and optimization for real-time or near-real-time inference using technologies such as ONNX, TensorRT, TorchScript, or equivalent frameworks.
• Experience establishing scalable MLOps workflows, including data and model versioning, experiment tracking, continuous integration/deployment, and production ML pipelines.
• Familiarity with medical device software development processes in regulated environments, including FDA regulations, design controls, V&V, and AI/ML-related FDA guidelines.
• Demonstrated ability to translate research-grade algorithms into robust, production-ready solutions for clinical or commercial applications.

The base pay for this position is $99,300.00 – $198,700.00. In specific locations, the pay range may vary from the range posted.