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Verified from career page · Posted 4w ago
Senior Solutions Architect, Higher Education and Research, AI and HP
NVIDIA · Architect, Solutions
Munich
- Posted
- 4w ago
- Workplace
- Remote
- Salary
- Not disclosed
- Visa sponsorship
- Not specified
Posted on 2 September 2026
Work model: Remote
Salary range not shared by the company
Visa sponsorship details unknown
This role has closed 3w ago ago. It's kept as a record — see NVIDIA's open roles or the similar live roles below.
See NVIDIA's open rolesNVIDIA 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 high-performance computing and scientific computing, and of how AI methods are reshaping both, 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 and AI methods for scientific discovery, 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 in the intersection of HPC and AI, running scientific workloads at scale on multi-node GPU systems and applying machine learning to the same problems.
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 computational science in combination with AI.
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 accelerated computing stack for science, powered by CUDA and CUDA-X libraries, e.g. PhysicsNeMo, Warp, cuEquivariance, ALCHEMI, Omniverse.
As a Solutions Architect, there is travel involved, as often the best way to figure things out is a faceto-face 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.
About NVIDIA
NVIDIA's European sites — Munich, Zurich, Kyiv, Amsterdam — cover deep learning, driver and systems software rather than sales engineering. One of the few places in Europe doing serious GPU and inference infrastructure work.