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

## Principal Engineer — Medical Imaging Reconstruction and Raw-to-Insights AIApplylocations:
US, WA, Redmond:
US, CA, Santa Claratime type:
Full time posted on:
Posted Todayjob requisition :
JR2020171

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

We are looking for a Principal Engineer to set the technical direction for raw-to-insights medical imaging: AI and signal-processing systems that operate directly on raw acquisition data (MRI k-space, CT projection data, PET list-mode, and other modalities) rather than on already-formed images. This is a founding, strategy-shaping role. You will define the reconstruction and acquisition-AI charter across our imaging stack, build the platform and standards that let an ecosystem build on top of your work, and partner closely with our existing ultrasound reconstruction effort to cover the full acquisition layer.

If you have built systems that moved imaging computation beyond the scanner and want to do it at industry scale to empower all medical imaging manufacturers, this role is for you.
** What you'll be doing:
*** 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.
* 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.
* Advancing AI-based reconstruction, denoising, motion correction, and uncertainty quantification — pushing image quality and trustworthiness from accelerated acquisitions.
* 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.
* 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.
* Serving as a company-level technical authority: influencing product and research strategy across multiple teams, representing NVIDIA to clinical, academic, and OEM partners, and mentoring senior engineers and researchers.
* Translating clinical and partner needs into architecture, then owning the decisions that determine the success of the reconstruction platform.
** What we need to see:
*** PhD in biomedical/electrical engineering, computer science, medical physics, or related field (or equivalent experience), and 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 — not just publications or one-off models.
* Fluency with modern deep-learning methods for inverse problems (reconstruction, denoising, super-resolution) and 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.
** Ways to stand out from the crowd:
*** You have created widely adopted open-source reconstruction software or community data standards that others build on.
* Familiarity with healthcare deployment realities: clinical workflow integration, regulatory/validation pathways, and security and compliance for…