Senior Solutions Architect, GPU Performance and LLM - Cloud Service Providers
Join our team at NVIDIA and help bring AI solutions to our largest customers. We are seeking an expert Solutions Architect to assist customers in building AI/ML and HPC software solutions at scale. As a member of our Solutions Architecture team, you will collaborate with strategic customers, providing end-to-end technology solutions and technical support based on our product strategy. Come join us!
Working with tech giants to develop and demonstrate solutions based on NVIDIA’s groundbreaking software and hardware technologies.
Partnering with Sales Account Managers and Developer Relations Managers to identify and secure business opportunities for NVIDIA products and solutions.
Serving as the main technical point of contact for customers engaged in the development of intricate AI infrastructure, while also offering support in understanding performance aspects related to tasks like large scale LLM training and inference.
Conducting regular technical customer meetings for project/product details, feature discussions, introductions to new technologies, performance advice, and debugging sessions.
Collaborating with customers to build Proof of Concepts (PoCs) for solutions to address critical business needs and support cloud service integration for NVIDIA technology on hyperscalers.
Analyzing and developing solutions for customer performance issues for both AI and systems performance.
BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.
8+ years of engineering (performance/system/solution) experience.
Hands-on experience building performance benchmarks for data center systems, including large scale AI training and inference.
Understanding of systems architecture including AI accelerators and networking as it relates to the performance of an overall application.
Effective engineering program management with the capability of balancing multiple tasks.
Ability to communicate ideas clearly through documents, presentations, and in external customer-facing environments.
Hands-on experience with Deep Learning frameworks (PyTorch, JAX, etc.), compilers (Triton, XLA, etc.), and NVIDIA libraries (TRTLLM, TensorRT, Nemo, NCCL, RAPIDS, etc.).
Familiarity with deep learning architectures and the latest LLM developments.
Background with NVIDIA hardware and software, performance tuning, and error diagnostics.
Hands-on experience with GPU systems in general including but not limited to performance testing, performance tuning, and benchmarking.
Experience deploying solutions in cloud environments including AWS, GCP, Azure, or OCI as well as knowledge of DevOps/MLOps technologies such as Docker/containers, Kubernetes, data center deployments, etc. Command line proficiency.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.- How much does the Senior Solutions Architect, GPU Performance and LLM - Cloud Service Providers at NVIDIA pay?
- The posting lists a range of $184K–$357K per year. Ranges reflect what NVIDIA publicly declared on the source posting.
- Where is this Senior Solutions Architect, GPU Performance and LLM - Cloud Service Providers role based?
- The role is based in US, CA, Santa Clara.
- What experience does NVIDIA expect for this role?
- The posting is tagged as a lead-level role, typically 7+ years of experience. Check the requirements section for specifics.
- How was this posting sourced?
- This role was pulled directly from NVIDIA's Workday careers site. Apply links open in the employer's own ATS — no reposts or aggregator middleware.
Apply links open in the employer's official ATS. Always verify recruitment messages on the company's careers page before sharing personal information.