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's 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 modern AI and HPC research workloads 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 Deep Learning, 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 o erings 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.
5+ years of hands-on experience running the large language model lifecycle across multi-node systems: training, fine-tuning, inference, serving, and/or agentic workflows.
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 on large language models or foundation models and their applications.
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 AI software stack, powered by CUDA and CUDA-X libraries, e.g. NVIDIA AI Enterprise, NeMo Framework, Megatron Bridge, NIM, TensorRT-LLM, Dynamo, NeMo Agent Toolkit, and Triton Inference Server, as well as the Nemotron open-model methodology.
As a Solutions Architect, there is travel involved (30% of the working time), as often the best way to figure things out is a face to-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.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:Germany: 85,800 EUR – 148,720 EUR for Level 3; and 108,075 EUR – 187,330 EUR for Level 4.
France: 69,000 EUR – 119,600 EUR for Level 3; and 87,750 EUR – 152,100 EUR for Level 4.
Skills Required
- Graduate degree from a leading university in a STEM-related discipline
- 5+ years of hands-on experience with the large language model lifecycle across multi-node systems, including training, fine-tuning, inference, serving, or agentic workflows
- 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, organized, and passionate learner
- Fluent English, both oral and written
- Comfort working with Python
- PhD from a leading university in a STEM-related discipline
- 3+ years of research on large language models or foundation models and their applications
- Track record of thought leadership through high-impact publications and academic conference or workshop presentations
- Understanding of scientific policy engagement, grant processes, or national and European research program structures
- Experience with NVIDIA's AI software stack, including CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NeMo Framework, Megatron Bridge, NIM, TensorRT-LLM, Dynamo, NeMo Agent Toolkit, Triton Inference Server, or Nemotron
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 — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
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Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
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Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
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.”
