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: As a Principal Scientist, you will help define the scientific and technical direction of AI across the structural heart disease journey. This role requires deep expertise in machine learning and scientific leadership, with the ability to evaluate new technologies, incubate novel capabilities, and guide teams developing AI-enabled products in regulated healthcare environments. Lead the design and development of scalable AI solutions that address complex business problems in partnership with business subject matter experts, product managers, data scientists, and engineering teams. Lead the development of domain-specific foundation models across structured and unstructured clinical and procedural datasets, including natural language, medical imaging, and waveforms. Drive research and experimentation in generative AI, multimodal AI, representation learning, self-supervised learning, agentic systems, and advanced machine learning techniques. Ensure models are developed with appropriate rigor for generalizability, safety, fairness, robustness, and real-world performance. Identify, evaluate, and incubate new technology opportunities, including simulation, computational modeling, digital twins, physics-informed AI, scientific machine learning, and next-generation foundation models. Deliver written quarterly technology-scouting positions that inform real investment decisions. Set scientific standards for the broader science group and provide technical leadership and mentorship to junior engineers and data scientists. Partner with engineering, quality, clinical, and regulatory teams to support development of AI-enabled products and digital solutions. Collaborate on model evaluation, validation, risk management, and deployment strategies for regulated healthcare applications. Assess operational feasibility by evaluating problem definitions, analyses, requirements, solution development, and proposed solutions. Build and maintain robust data pipelines to train, test, and evaluate large language models (LLMs) and traditional machine learning models; engineer solutions using AI/ML frameworks and libraries. Stay current with generative AI and traditional AI/ML research and trends and incorporate relevant advances into product offerings. Improve and optimize model performance to ensure robustness, scalability, and efficiency. 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 performance drift or shifts across different datasets. Ensure code and model development are well documented and follow Edwards engineering practices. What you’ll need (Required): Bachelor’s degree plus 6 years of experience in AI/ML engineering, with demonstrated success working with cross-functional IT and/or business teams on enterprise-level, complex, or novel system implementations. Proven experience designing, training, and validating machine learning models end to end. Deep technical breadth across at least two of the following: natural language processing/agentic AI, computer vision, time-series and signal modeling, or computational modeling and simulation. What else we look for (Preferred): MS or PhD (preferred, but not required) in computer science, machine learning, biomedical engineering, or a related field, or equivalent industry depth. 10 to 15 years in applied machine learning or AI research, with a track record of shipping models into production, not just publications. Proficiency with common ML and DL libraries (e.g., scikit-learn, NumPy, SciPy, PyTorch, TensorFlow/Keras), NLP/LLM libraries and APIs (e.g., Hugging Face libraries, LangChain or LlamaIndex, frontier model APIs, cloud and data platform APIs), and computer vision libraries (e.g., OpenCV, scikit-image, PIL, torchvision). Senior individual-contributor technical leadership: provide influence on technical direction and provide mentoring without direct people leadership. Prior experience in a cardiovascular, structural heart, or broader medtech environment. Experience deploying AI/ML models to production systems, not just research models. Experience in a leadership or senior technical role driving AI/ML strategy. Experience with modern deep learning model architectures, such as convolutional neural networks (including U-Nets), transformers (including large language models and vision transformers), diffusion models, and graph neural networks. Experience with learning paradigms and system designs, such as generative AI, foundation models, self-supervised learning (e.g., masked autoencoders and contrastive learning), multimodal learning, retrieval-augmented generation, agentic systems, and physics-informed machine learning. Experience with cloud computing environments (particularly Amazon Web Services and Microsoft Azure), as well as remote and distributed computing. Strong technical programming skills, particularly in Python; experience with R, SQL, Julia, Rust, HTML, JavaScript, CSS, Java, C , or similar languages is also valued. Experience working with data platforms, including Snowflake, Databricks, and/or Palantir. Experience supporting development of regulated software, Software as a Medical Device (SaMD), or AI-enabled medical devices. Familiarity with design controls, verification and validation activities, risk management, and Good Machine Learning Practice (GMLP). Experience with version control systems such as Git and agile development methodologies. A peer-reviewed publication record or equivalent recognized technical standing. Relevant experience and training in the technologies used for development. Excellent analytical, organizational, time management, information-seeking, and attention to detail skills. Excellent written and verbal communication, negotiation, interpersonal, and relationship management skills, with the ability to drive achievement of objectives. Ability to manage multiple tasks, competing priorities, and long-term goals in a fast-paced environment. Ability to interact professionally with colleagues at all organizational levels. Ability to follow all company requirements, including applicable health and safety protocols, and take appropriate measures to prevent injury, protect the environment, and prevent pollution 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 $132,000 to $186,000 (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

Lead the design and development of scalable AI solutions that address complex business problems in partnership with business subject matter experts, product managers, data scientists, and engineering teams.
Lead the development of domain-specific foundation models across structured and unstructured clinical and procedural datasets, including natural language, medical imaging, and waveforms.
Drive research and experimentation in generative AI, multimodal AI, representation learning, self-supervised learning, agentic systems, and advanced machine learning techniques.
Ensure models are developed with appropriate rigor for generalizability, safety, fairness, robustness, and real-world performance.
Identify, evaluate, and incubate new technology opportunities, including simulation, computational modeling, digital twins, physics-informed AI, scientific machine learning, and next-generation foundation models.
Deliver written quarterly technology-scouting positions that inform real investment decisions.
Set scientific standards for the broader science group and provide technical leadership and mentorship to junior engineers and data scientists.
Partner with engineering, quality, clinical, and regulatory teams to support development of AI-enabled products and digital solutions.
Collaborate on model evaluation, validation, risk management, and deployment strategies for regulated healthcare applications.
Assess operational feasibility by evaluating problem definitions, analyses, requirements, solution development, and proposed solutions.
Build and maintain robust data pipelines to train, test, and evaluate large language models (LLMs) and traditional machine learning models; engineer solutions using AI/ML frameworks and libraries.
Stay current with generative AI and traditional AI/ML research and trends and incorporate relevant advances into product offerings.
Improve and optimize model performance to ensure robustness, scalability, and efficiency.
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 performance drift or shifts across different datasets.
Ensure code and model development are well documented and follow Edwards engineering practices.
Bachelor’s degree plus 6 years of experience in AI/ML engineering, with demonstrated success working with cross-functional IT and/or business teams on enterprise-level, complex, or novel system implementations.
Proven experience designing, training, and validating machine learning models end to end.
Deep technical breadth across at least two of the following: natural language processing/agentic AI, computer vision, time-series and signal modeling, or computational modeling and simulation.
10 to 15 years in applied machine learning or AI research, with a track record of shipping models into production, not just publications.
Senior individual-contributor technical leadership: provide influence on technical direction and provide mentoring without direct people leadership.
Prior experience in a cardiovascular, structural heart, or broader medtech environment.
Experience deploying AI/ML models to production systems, not just research models.

Nice-to-haves

MS or PhD (preferred, but not required) in computer science, machine learning, biomedical engineering, or a related field, or equivalent industry depth.

Application prep

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

Keywords to mirror

aimachine learningmlllmazurepythonsqlrustjavascriptjavaagile

Platform: workday
Effort: long

Benefits

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