Jobs at NVIDIA

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Lead GTM technical engagements with data-platform ISVs to design architectures, build POCs, benchmarks, and enablement for NVIDIA acceleration across analytics, databases, and GenAI workflows. Create reference assets, demos, and training while partnering with product, engineering, sales, and customers.

Design and evaluate GPU-native numerical algorithms for engineering simulation: develop and benchmark linear/nonlinear solvers, multigrid, preconditioners, mixed-precision and matrix-free methods, prototype research code, and transition promising approaches into NVIDIA software in collaboration with research and engineering teams.

Invent and research AI-native numerical algorithms that integrate ML with classical solvers on GPUs. Develop learned preconditioners, AI-guided multigrid, differentiable solver components, and solver-in-the-loop pipelines. Define numeric evaluation metrics, collaborate across research and product teams, and help set NVIDIA's applied research agenda for solver intelligence.

The role focuses on developing and maintaining continuous integration systems for NVIDIA's AI software ecosystem, ensuring optimal performance and quality of software stacks.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

The role involves optimizing GPU performance for neural reconstruction workflows, analyzing bottlenecks, and collaborating with engineers to enhance training and rendering systems.

Create developer-facing technical materials and demos for NVIDIA Omniverse libraries, SDKs, and APIs. Produce tutorials, reference architectures, blogs, videos, and launch collateral to drive developer adoption for simulation, rendering, robotics, synthetic data, and industrial digitalization workflows. Collaborate with engineering, product, marketing, and sales to identify gaps in the developer journey and translate platform capabilities into clear technical narratives and hands-on content.

Lead architecture for cloud-networking and design system architectures for DPUs and NICs. Build end-to-end solutions from application to hardware, write architecture specifications, evaluate new technologies, develop POCs, and collaborate globally with SW/HW teams and customers.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Lead development and execution of a global risk and compliance framework. Manage a high-performing compliance team, partner with cross-functional groups to identify and mitigate risks, run assessments, oversee policy, training, and audits, advise senior leadership, and drive continuous improvement of risk and compliance programs.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

Provide technical support to NVIDIA Enterprise customers by triaging and resolving issues, working with engineering on reproductions/logs, applying AI/ML and agentic AI to build support tools and products, documenting interactions and knowledge, and owning cases to resolution (occasional weekend/holiday support).

Provide advanced support and root-cause analysis for NVIDIA networking technologies in AI clusters. Reproduce and resolve customer issues, develop fixes and tools, analyze performance/tuning for high-performance lossless networks, collaborate with R&D and customers, and occasionally support outside regular hours.

Lead technical customer engagements to design, build, and deploy GPU-accelerated AI/ML, DL, and data analytics solutions on cloud platforms. Create PoCs, run demos, collaborate with sales and engineering, deliver workshops and technical presentations, and troubleshoot customer deployments at scale.

Research and implement GPU-acceleration techniques for high-performance databases and ETL. Analyze and optimize data-intensive workloads, collaborate with hardware, systems, and research teams to influence next-generation architectures and programming models, and publish/share findings with customers and the community.

Research and develop techniques to accelerate large-scale applications on advanced CPU platforms. Analyze and optimize database and analytics workloads for CPU performance, collaborate with internal and external experts, publish findings, and influence next-generation hardware/software design.

Lead a team of performance engineers to optimize complex workloads on NVIDIA's CPUs and GPUs, driving technical excellence and innovation while mentoring team members and collaborating with cross-functional partners.

Provide advanced technical support for NVIDIA InfiniBand, NVLink and GPU-based HPC/AI infrastructure. Troubleshoot installations, networking and interoperability issues on multi-distro Linux, reproduce and resolve customer problems, use packet analysis and cluster tools, and collaborate with engineering, marketing, and support teams to improve processes and documentation.