Senior Solutions Architect – Medical Devices

Senior Solutions Architect – Medical Devices

Requirements

• MS, PhD or equivalent experience in Biomedical Engineering, Electrical Engineering, Computer Science, Mathematics or closely related fields.
• 5+ years of work-related experience in software development, machine learning or high-performance computing.
• Scientific computing experience including software development in scientific programming languages like C/C++, Python, and/or CUDA.
• Understanding of how to apply multimodal and generative AI to answer scientific questions.
• Experience with modern Deep Learning software architecture and frameworks.
• Excellent communication skills particularly in the presentation of highly technical material.
• Must enjoy interacting with forward-thinking people, life-long learning, and staying on the cutting edge of the domain.

Responsibilities

• Partner with our business/account team working with customers to develop a keen understanding of their goals, strategies, and technical needs and to help define and deliver high-value solutions meeting these needs.
• Optimize workloads using NVIDIA’s computing platform.
• Work to design NVIDIA’s HW/SW into a new generation of software defined medical devices.
• Stay up to date on the state of the art in image analysis, sensor processing, robotics & surgical data science to apply the latest advancements in the field to customer needs.
• Document what you know and teach others. This can vary from building targeted training for partners and other Solutions Architects, to writing whitepapers, blogs, and wiki articles, to simply working through hard problems with a customer on a whiteboard.
• Partner strategically with lighthouse customers and industry-specific solution partners targeting our computing platform.
• We make heavy use of conferencing tools, but some travel is required for this role. You are empowered to find the best way to get your job done and make our customers successful.

Preferred Qualifications

• Hands on experience applying accelerated computing and AI in one or more of the following domains – medical imaging, medical robotics, digital surgery.
• Experience with medical device development lifecycle or SaMDs.
• Extensive deep learning knowledge with practical experience with relevant frameworks and model architectures (e.g., CNNs, LLMs, VLMs, VLAs), training robotic policies, training from simulation / Digital Twins.
• Proficiency in deploying AI models and optimizing inference using TensorRT, ONNX Runtime, Triton, or TensorRT-LLM is a plus.
• Published record of thought leadership in a technical area or industry segment.