Innovation starts from the heart. Our Advanced Innovation & Technology (AI&T) teams harness the imagination, courage, and resourcefulness to think beyond what’s currently possible, and create solutions for patients many years into the future. If you’re an early-stage innovator, then Edwards AI&T team is the place for you to take the next steps in your career. We’ll give you the tools and resources you need to create groundbreaking innovations that shape the future of structural heart technology.
About the Role
We are looking for a Senior Principal, Algorithms Engineer to join the R&D team developing medical imaging AI solutions focused on segmentation, analysis, and 3D reconstruction to develop medical applications.
This is a hands‑on role with responsibility for building, integrating, and maintaining end‑to‑end ML-driven systems, from data ingestion and model development through inference, deployment, and application integration.
How you will make an impact:
Architecture & Engineering
- Contribute to system architecture and engineering standards across ML, data, and deployment components.
- Design and implement end‑to‑end ML systems, covering data pipelines, model training, inference services, and application integration.
- Participate in design reviews and technical decision‑making with a strong hands‑on implementation focus.
Medical Imaging & Machine Learning
- Apply deep understanding of ML development practices, including model architecture design, training strategies, evaluation, and inference optimization.
- Optimize models for volumetric medical data with respect to accuracy, memory usage, and runtime performance.
- Develop and optimize deep learning models for medical image segmentation.
Data Handling & Preprocessing
- Build and maintain production‑grade pipelines for large DICOM datasets.
- Implement robust preprocessing and augmentation workflows supporting both training and inference at scale.
Integration, Deployment & MLOps
- Integrate ML models into production medical applications and backend services.
- Design and implement inference and deployment strategies, including model versioning, performance tuning, and reliability in AWS‑based environments.
- Work closely with other software developers to integrate ML solutions into the broader platform and end‑user applications.
Quality, Validation & Collaboration
- Work closely with SQA & Automation engineers to design ML systems for testability and to implement advanced validation strategies, including end‑to‑end, regression, and performance validations.
- Support CI/CD pipelines by enabling automated testing and validation of ML models, data pipelines, and application integrations.
Collaboration & R&D
- Collaborate with engineering, QA, clinicians, and product teams to validate functionality, performance, and usability.
- Stay current with advances in modern ML architectures, training techniques, and deployment practices, contributing hands‑on to R&D initiatives.
Whatyou’llneed:
- MSc. in Computer Science, Software Engineering, Biomedical Engineering, or equivalent practical experience.
- 8+ years of professional Algorithms and Software Development experience, including hands‑on ownership of end‑to‑end ML systems from design to production.
- Deep knowledge and understanding of machine learning development, including modern architectures, training methodologies, inference optimization, and deployment.
- Strong Python skills with proven hands‑on experience using PyTorch.
- Proven experience with
- U‑Net variants (3D U‑Net, nnU‑Net)
- 3D CNNs
- MONAI
- 3D Slicer
- YOLO variants
- and medical image segmentation pipelines.
- Solid knowledge of medical imaging data formats (DICOM, NIfTI).
- Strong foundation in image processing and computer vision.
- Experience optimizing GPU‑accelerated and performance‑sensitive ML workloads.
- Practical experience with CI/CD pipelines, automated testing, and Git‑based workflows.
- Excellent documentation skills; strong English communication; collaborative and accountable team player.
What else we look for:
- Advanced degree (M.Sc./Ph.D.) in a relevant field.
- Experience deploying ML systems on AWS (including SageMaker).
- Experience working in regulated or medical software environments.
- Background in 3D visualization or interactive medical applications.
- Full‑stack experience (Node.js, React, PostgreSQL).
