NVIDIA has been redefining computer graphics and accelerated computing for three decades. Today, we are tapping into the unlimited potential of AI to define the next era of computing. Doing what has never been done before takes vision, innovation, and exceptional talent. As an NVIDIAN, you will be immersed in a diverse, supportive environment where everyone is encouraged to do their best work and make a lasting impact on the world.
We are looking for a Solutions Architect to work with academia and research partners. Solutions Architects are the primary technical contacts for our customers and engage deeply with researchers and application developers to drive the adoption of NVIDIA technology. We seek an individual who combines an intricate understanding of Multimodal AI and Physical AI with expertise in accelerated computing and architecture.
What you will be doing:
Partner directly with leading research labs, researchers, and university faculty as a trusted technical advisor: develop a keen understanding of their scientific goals and drive joint research projects at supercomputing scale.
Identify and accelerate high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects, and help researchers amplify their impact through publications, conference presentations, and technical content.
Advocate for accelerated computing, robotics, Multimodal AI and Physical AI, and deliver hands-on trainings, workshops, lectures and demonstrations across NVIDIA's platforms, and mentor power users to become NVIDIA champions.
Track emerging research trends and turn gaps between researcher needs and NVIDIA's offerings into prototypical solutions and direct feedback to NVIDIA Engineering.
Maintain deep expertise in your domain while staying versatile across NVIDIA's full platform: GPUs, CPUs, networking, and software
What we need to see:
A graduate degree from a leading university in a STEM related discipline or equivalent experience.
5+ years across the multimodal and world model lifecycle on multi-node GPU systems: video and image data curation at scale, pre-training and post-training, evaluation, and efficient inference.
Strong collaboration and communication skills, with the ability to build relationships with academic and research stakeholders, and to communicate complex ideas clearly to both expert and non-expert audiences.
Action oriented, analytical, self-motivated, and a passionate learner, with excellent organization skills to work in a heavily multi-tasked environment.
Fluent in English, both oral and written, and comfortable working in Python
Ways to stand out from the crowd:
A PhD from a leading university in a STEM related discipline, with 3+ years of research in the domain.
A track record of thought leadership: high-impact publications, talks at academic conferences and workshops, as well as good understanding of scientific policy engagement, grant processes, or national/European research program structures.
Experience with NVIDIA's stack for visual and multimodal AI, built on CUDA and CUDA-X, including Cosmos world foundation models, NeMo Framework and Megatron for multimodal model training, multimodal Nemotron methodology, Isaac robotics platform, NuRec for neural reconstruction, TensorRT and NIM for deployment, and domain frameworks such as MONAI for medical imaging.
As a Solutions Architect, there is travel involved (20% of the working time), as often the best way to figure things out is a face-toface meeting, but the job is not life on the road. We make heavy use of conferencing tools, and you are empowered to figure out how to get the job done and do what it takes to make our customers successful
Skills Required
- Graduate degree from a leading university in a STEM-related discipline or equivalent experience
- 5+ years of experience across the multimodal and world model lifecycle on multi-node GPU systems
- Experience with video and image data curation at scale, pre-training, post-training, evaluation, and efficient inference
- Strong collaboration and communication skills with academic and research stakeholders
- Ability to communicate complex ideas clearly to expert and non-expert audiences
- Action-oriented, analytical, self-motivated, passionate learner with strong organization skills
- Fluent English, oral and written
- Comfort working in Python
- PhD from a leading university in a STEM-related discipline
- 3+ years of research in the domain
- Track record of high-impact publications and talks at academic conferences and workshops
- Understanding of scientific policy engagement, grant processes, or national/European research program structures
- Experience with NVIDIA's visual and multimodal AI stack, including CUDA, CUDA-X, Cosmos, NeMo, Megatron, Nemotron, Isaac, NuRec, TensorRT, NIM, or MONAI
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
