Principal Engineer — Medical Imaging Reconstruction and Raw-to-insights AI

NVIDIA NVIDIA · Semiconductors · Redmond, WA +1

NVIDIA is seeking a Principal Engineer to lead the technical direction for raw-to-insights medical imaging AI and signal-processing systems. This role involves defining strategy, building platforms, and advancing AI-based reconstruction techniques for modalities like MRI, CT, and PET, with a focus on GPU acceleration and real-time processing.

What you'd actually do

  1. Defining the technical and architectural strategy for medical image reconstruction and raw-to-insights AI across modalities (MRI, CT, PET), and establishing the reference architecture for imaging reconstruction on Holoscan and accelerated compute.
  2. Designing and guiding GPU-accelerated, real-time and distributed reconstruction pipelines that span edge devices and shared cloud compute, with the latency, throughput, and reliability that clinical use demands.
  3. Advancing AI-based reconstruction, denoising, motion correction, and uncertainty quantification — pushing image quality and trustworthiness from accelerated acquisitions.
  4. Driving raw-data access, interoperability, and standardization (cross-vendor acquisition formats, AI-ready datasets) so that researchers and partners across the ecosystem can build on a common foundation.
  5. Partnering with the ultrasound raw-to-insights lead so the modalities are complementary, the platform philosophy is shared, and the combined effort covers the entire acquisition layer end to end.

Skills

Required

  • PhD in biomedical/electrical engineering, computer science, medical physics, or related field (or equivalent experience)
  • 12+ years of relevant experience building computational imaging or signal-processing systems
  • Recognized and sought-out for expertise in reconstructing medical images and understanding image formation physics
  • Proficient in at least one modality (MRI, CT, or PET) at the raw-signal level (e.g., k-space, projection, or list-mode data)
  • Deep, hands-on command of GPU-accelerated computing and the infrastructure required to deploy reconstruction at scale (C++ and/or Python, CUDA, real-time/streaming pipelines, edge-to-cloud architectures)
  • A track record of building platforms, frameworks, or open-source systems that created leverage for an ecosystem beyond your immediate team
  • Fluency with modern deep-learning methods for inverse problems (reconstruction, denoising, super-resolution)
  • A clear point of view on validation, uncertainty, and clinical trustworthiness
  • The ability to operate as a cross-organization technical leader: setting direction, influencing through others, and shaping strategy at the department and company level

Nice to have

  • created widely adopted open-source reconstruction software or community data standards
  • Familiarity with healthcare deployment realities: clinical workflow integration, regulatory/validation pathways, and security and compliance for patient data
  • A history of working across academia, clinical sites, and instrument manufacturers, and of moving research prototypes into real deployment
  • Exposure to physics-informed AI, foundation models operating on raw signals, or adjacent simulation and synthetic-data work

What the JD emphasized

  • recognized and sought-out for expertise
  • track record of building platforms, frameworks, or open-source systems
  • deep, hands-on command of GPU-accelerated computing and the infrastructure required to deploy reconstruction at scale

Other signals

  • GPU-accelerated reconstruction pipelines
  • AI-based reconstruction
  • building platforms, frameworks, or open-source systems