About this role
About the Role Join the Computed Tomography (CT) High-Performance Computing team and help build the future of medical imaging. Your work will reach clinicians and patients in Philips CT scanners around the world. Your role: Design and develop our next-generation, GPU-accelerated platform architecture. Partner with architects, engineers, physicists, and stakeholders to translate complex signal and image processing algorithms into scalable, high-performance implementations—balancing image quality against computational cost. Collaborate with CT platform teams to design, develop, test, maintain, and deploy an industry-leading CT reconstruction software platform. Analyze algorithm specifications and prototypes to identify efficient compute designs. Apply AI both ways—using AI-assisted development to speed feature delivery, and building learned models that approximate expensive physics with fast, accurate equivalents. Deliver solutions with a strong focus on balancing image quality against computational cost. What You'll Do
- Design and develop our next-generation, GPU-accelerated platform architecture for CT reconstruction.
- Partner with architects, engineers, physicists, and stakeholders to translate complex signal and image processing algorithms into scalable, high-performance implementations.
- Collaborate with CT platform teams to design, develop, test, maintain, and deploy an industry-leading CT reconstruction software platform.
- Analyze algorithm specifications and prototypes to identify efficient compute designs.
- Apply AI to speed feature delivery and build learned models that approximate expensive physics with fast, accurate equivalents.
- Deliver solutions with a strong focus on balancing image quality against computational cost. What We're Looking For
- Experience in designing and developing GPU-accelerated software platforms (C++/CUDA).
- Strong background in signal and image processing, and high-performance computing.
- Ability to translate algorithm specifications into scalable, high-performance implementations.
- Cross-functional collaboration with architects, engineers, scientists, and stakeholders.
- Interest in applying AI to accelerate development and to create fast, learned models for physics-based problems.