Principal Scientist, AI / ML

How you will make an impact

• 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.