Solutions Architect, Higher Education and Research
NVIDIA
Job Overview
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Job Description
About the Role
The Solutions Architect, Higher Education and Research at NVIDIA is a role for computational scientists, engineers, or data scientists who are passionate about artificial intelligence and high performance computing. You will work with customers in universities and research institutions to deliver innovative solutions using NVIDIA's accelerated computing platforms.
Key Responsibilities
- Lead HPC and AI technical engagements with academic and research customers.
- Engage with customers to understand technical goals and requirements.
- Develop and document solutions including training, whitepapers, and technical articles.
- Collaborate with cross-functional teams including sales, program management, and business development.
- Travel up to 20% for customer engagements.
What You Need
- BS, MS or PhD in Engineering, Mathematics, Physical Sciences, or Computer Science (or equivalent experience).
- 5+ years of relevant work experience.
- Experience in porting/optimizing scientific applications on GPUs.
- Strong programming, problem-solving and debugging skills.
- Excellent presentation, communication and collaboration skills.
Preferred Qualifications
- Experience supporting HPC or AI in academic research.
- Full-stack scientific computing experience (C/C++, Python, CUDA, OpenACC, OpenMP, MPI).
- Experience with containerization and orchestration tools (Docker, Kubernetes, Slurm, etc.).
Compensation & Benefits
Base salary ranges based on level and location, with additional equity and benefits. This posting is for an existing vacancy, open until January 13, 2026.
Key skills/competency
- HPC
- AI
- GPU
- Deep Learning
- Scientific Computing
- Customer Engagement
- Technical Training
- Programming
- Containerization
- Collaboration
How to Get Hired at NVIDIA
- Research NVIDIA's culture: Study their mission, values, and tech outcomes.
- Customize your resume: Tailor technical skills and project experiences.
- Highlight HPC expertise: Emphasize your GPU and AI projects.
- Prepare for technical interviews: Review scientific computing and programming challenges.
- Network within academia: Leverage academic collaborations and LinkedIn.
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