Senior Solutions Architect, Physical AI Cloud

Experteer Overview In this role you help partners deploy NVIDIA’s Physical AI workloads at scale, focusing on GPU-accelerated robotics pipelines and cloud-native architectures. You will design scalable, observable Kubernetes-based environments for distributed robotics tasks and guide cross-functional teams to adopt NVIDIA frameworks. You influence architecture decisions that improve scheduling, storage, networking, and GPU utilization across hybrid infrastructure. This is a hands-on, collaboration-heavy position with a strong impact on robotics AI and simulation initiatives. Compensation / Benefits * Design and scale Kubernetes-native environments for distributed robotics workloads * Build scalable, observable pipelines for GPU-accelerated Physical AI workflows * Develop data factories for ingestion, preprocessing, synthetic data generation, training, simulation, and evaluation * Translate customer requirements into optimized cloud-native architectures and improve resource utilization * Accelerate distributed inference using NVIDIA technologies (NIM, TensorRT-LLM, vLLM, SGLang) * Provide technical guidance and mentorship to customers and collaborate with product, engineering, and business teams Tasks * BS in Computer Science, Computer Engineering, or related field; or equivalent experience * 5+ years in Solution Architecture or Infrastructure Engineering for AI/ML systems in cloud environments * Experience scaling robotics workloads (multimodal model training, inference, robot learning, simulation) * Hands-on experience with Kubernetes-based platforms for distributed GPU/AI workloads * Strong networking, storage, and workflow orchestration skills; DevOps practices (GitOps, IaC, Observability) * Excellent communication skills to convey complex concepts to diverse audiences Key requirements * equity * benefits package

• Accelerate distributed inference using NVIDIA technologies (NIM, TensorRT-LLM, vLLM, SGLang) * Provide technical guidance and mentorship to customers and collaborate with product, engineering, and business teams Tasks * BS in Computer Science, Computer Engineering, or related field; or equivalent experience * 5+ years in Solution Architecture or Infrastructure Engineering for AI/ML systems in cloud environments * Experience scaling robotics workloads (multimodal model training, inference, robot learning, simulation) * Hands-on experience with Kubernetes-based platforms for distributed GPU/AI workloads * Strong networking, storage, and workflow orchestration skills; DevOps practices (GitOps, IaC, Observability) * Excellent communication skills to convey complex concepts to diverse audiences Key requirements * equity * benefits package