Job description

At Edwards Lifesciences, the Advanced Innovation & Technology (AI&T) teams harness imagination, courage, and resourcefulness to think beyond what’s currently possible and create solutions for patients many years into the future. As part of AI&T, we re building an AI Center of Excellence (COE) focused on applying the power of AI to help patients live longer, healthier lives. As we expand our focus across the structural heart disease journey, AI is creating new opportunities to better understand and improve patient care. Our teams are applying AI to support clinical decision-making, identify patients earlier, develop digital solutions that complement our therapies, and help employees work more efficiently so they can focus on work that matters most. We re building an AI COE that collaborates across our innovation-focused teams throughout our global organization, helping accelerate innovation and build AI capabilities that create lasting impact for patients, clinicians, and employees. Together, we re committed to applying AI responsibly and thoughtfully while keeping patients at the center of everything we do. This is a rare opportunity to help define how AI is applied across the structural heart disease journey while shaping the next generation of therapies, digital solutions, and patient experiences. We’ll give you the tools and resources you need to create groundbreaking innovations that shape the future of structural heart technology. How you will make an impact: This role is focused on developing advanced computer vision models for medical imaging applications across echocardiography, computed tomography, and other cardiovascular imaging modalities to support efforts across the enterprise, working at the intersection of medical image analysis and product delivery. You will lead the design, development, validation, and translation of deep learning (DL) models that support research, product development, and clinical decision-making. You will work closely with clinical, engineering, regulatory, and product teams to ensure that AI solutions meet the scientific rigor, performance expectations, and quality requirements necessary for deployment in regulated healthcare environments. The role requires independence and fast-paced, responsible development of DL models with the methodological rigor that is necessary to clear established evaluation and regulatory standards. Key responsibilities Design, train, fine-tune, and validate deep learning computer vision models for medical imaging applications. Develop AI capabilities for image quality assessment, anatomical segmentation, landmark detection, geometric measurement, patient screening, procedural planning, and clinical decision support. Advance the development of in-house medical imaging foundation models through large-scale supervised, self-supervised, and transfer-learning approaches. Ensure models generalize across multiple imaging vendors and acquisition protocols, not only a single reference dataset. Partner closely with AI Evaluation, Quality, Regulatory Affairs, and Software Engineering teams to support verification, validation, risk assessment, and regulatory documentation activities. Rigorously evaluate models for systemic bias and stand up MLOps frameworks to monitor for performance drift or shift across different datasets. Drive research and experimentation to explore new AI and machine learning techniques, tools, and frameworks. Collaborate with data scientists, software engineers, product managers, and business leaders to define requirements and develop AI and machine learning solutions. Design, develop, and deploy scalable, efficient AI and machine learning models for prototypes and production use. Build end-to-end data pipelines for collecting, processing, and analyzing large-scale datasets. Optimize model performance to improve robustness, scalability, and efficiency. Participate in agile development processes, including sprint planning, daily stand-ups, prototype demonstrations, and milestone updates. Ensure code and model development are well-documented and follow engineering best practices. Perform other incidental duties as assigned. What you’ll need (Required): Bachelor’s degree in engineering or a related technical field, plus four years of AI / ML engineering experience, including successful collaboration with cross-functional teams on complex, enterprise-level, or novel system implementations. Proven experience designing, training, and validating deep learning computer vision models end to end. Strong experience with medical image analysis, including one or more of the following: segmentation, registration, landmark detection, image quality assessment, anatomical measurement, or disease classification. What else we look for (Preferred): MS or PhD (preferred, but not required) in computer vision, biomedical engineering, or a related field, or equivalent industry depth. 5 to 10 years’ experience in computer vision or medical image analysis, with direct experience handling failure modes, including generalizability across different datasets, institutions, and vendors. Experience working with DICOM imaging data and large-scale imaging datasets. Proficiency with common machine learning, deep learning, and computer vision libraries, such as scikit-learn, NumPy, SciPy, PyTorch, TensorFlow/Keras, OpenCV, scikit-image, PIL, and torchvision. Strong knowledge of machine learning algorithms, statistics, and data structures. Experience with modern computer vision deep learning model architectures and learning paradigms such as transformers (including vision transformers), convolutional neural networks (CNNs, including U-Nets), diffusion models, and self-supervised learning (e.g., masked autoencoders and contrastive learning frameworks). Experience with modern computer vision model architectures and learning paradigms, including vision transformers, convolutional neural networks such as U-Nets, diffusion models, and self-supervised learning approaches such as masked autoencoders and contrastive learning frameworks. Experience with cloud computing environments (particularly Amazon Web Services and Microsoft Azure), as well as remote computing and distributed computing. Proficiency in Python; experience with R, SQL, Julia, Rust, C , or a similar language is also valued. Experience working with data platforms, including Snowflake, Databricks, and/or Palantir. Experience deploying machine learning solutions into production systems. Experience supporting the development of regulated software, Software as a Medical Device, or AI-enabled medical devices. Familiarity with design controls, verification and validation activities, risk management, and Good Machine Learning Practice (GMLP). Demonstrated technical leadership through publications, patents, open-source contributions, product launches, or industry recognition. Experience with version control systems such as Git and with agile development methodologies. Preferred experience collaborating with executive-level management, external vendors, and team members across regional and global offices. Effective communication, collaboration, and problem-solving skills. Excellent attention to detail and the ability to manage competing priorities in a fast-paced environment. Ability to follow all company policies and requirements, including Environmental Health & Safety protocols, and to take appropriate measures to help prevent injuries and protect the environment within the scope of the role. Aligning our overall business objectives with performance, we offer competitive salaries, performance-based incentives, and a wide variety of benefits programs to address the diverse individual needs of our employees and their families. For California (CA), the base pay range for this position is $119,000 to $168,000 (highly experienced). The pay for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience). Applications will be accepted while this position is posted on our Careers website. Edwards is an Equal Opportunity/Affirmative Action employer including protected Veterans and individuals with disabilities.

Requirements

Must-haves

Design, train, fine-tune, and validate deep learning computer vision models for medical imaging applications.
Develop AI capabilities for image quality assessment, anatomical segmentation, landmark detection, geometric measurement, patient screening, procedural planning, and clinical decision support.
Advance the development of in-house medical imaging foundation models through large-scale supervised, self-supervised, and transfer-learning approaches.
Ensure models generalize across multiple imaging vendors and acquisition protocols, not only a single reference dataset.
Partner closely with AI Evaluation, Quality, Regulatory Affairs, and Software Engineering teams to support verification, validation, risk assessment, and regulatory documentation activities.
Rigorously evaluate models for systemic bias and stand up MLOps frameworks to monitor for performance drift or shift across different datasets.
Drive research and experimentation to explore new AI and machine learning techniques, tools, and frameworks.
Collaborate with data scientists, software engineers, product managers, and business leaders to define requirements and develop AI and machine learning solutions.
Design, develop, and deploy scalable, efficient AI and machine learning models for prototypes and production use.
Build end-to-end data pipelines for collecting, processing, and analyzing large-scale datasets.
Optimize model performance to improve robustness, scalability, and efficiency.
Participate in agile development processes, including sprint planning, daily stand-ups, prototype demonstrations, and milestone updates.
Ensure code and model development are well-documented and follow engineering best practices.
Perform other incidental duties as assigned.
Proven experience designing, training, and validating deep learning computer vision models end to end.
Strong experience with medical image analysis, including one or more of the following: segmentation, registration, landmark detection, image quality assessment, anatomical measurement, or disease classification.
5 to 10 years’ experience in computer vision or medical image analysis, with direct experience handling failure modes, including generalizability across different datasets, institutions, and vendors.
Experience working with DICOM imaging data and large-scale imaging datasets.
Proficiency with common machine learning, deep learning, and computer vision libraries, such as scikit-learn, NumPy, SciPy, PyTorch, TensorFlow/Keras, OpenCV, scikit-image, PIL, and torchvision.
Strong knowledge of machine learning algorithms, statistics, and data structures.
Experience with cloud computing environments (particularly Amazon Web Services and Microsoft Azure), as well as remote computing and distributed computing.
Proficiency in Python; experience with R, SQL, Julia, Rust, C , or a similar language is also valued.

Nice-to-haves

MS or PhD (preferred, but not required) in computer vision, biomedical engineering, or a related field, or equivalent industry depth.
Preferred experience collaborating with executive-level management, external vendors, and team members across regional and global offices.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningagilemlazurepythonsqlrust

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision