Senior Solutions Architect, Physical AI Cloud

Location: United States We're building a group of innovators to assist enterprises in deploying and accelerating NVIDIA’s three computer workloads for Physical AI. These include robotics simulation, synthetic data generation, multi-step model training, and inference, all on a large scale! We are seeking a hands-on Solutions Architect with deep expertise in backend infrastructure, inference and cloud-native applications to design and scale Kubernetes-native environments for distributed Robotics workloads. This role offers an outstanding chance to build within the rapidly growing field of Robotics AI & Simulation. You’ll work closely with our product management, engineering, and business teams to drive the adoption of NVIDIA's groundbreaking Physical AI technologies with our key ecosystem partners! What you’ll be doing: Help partners build scalable, observable, GPU-accelerated Physical AI pipelines through agentic workflows, cloud-native technologies, and NVIDIA frameworks such as OSMO. Support development of Physical AI data factories for data ingestion, preprocessing, annotation, filtering, synthetic data generation, training, simulation, and evaluation. Develop a deep understanding of robotics workload scaling and translate customer requirements into optimized cloud-native architectures, improving scheduling, cost, storage access, networking, and GPU utilization across hybrid infrastructure. Accelerate distributed inference using NVIDIA technologies such as NIM, TensorRT-LLM, vLLM, and SGLang. Collaborate with business, engineering, and product teams while providing technical guidance and mentorship to customers implementing Physical AI at scale. What we need to see: BS in Computer Science, Computer Engineering, or a related field, or equivalent experience. 5+ Years of experience in Solution Architecture or Infrastructure Engineering, advancing AI/ML systems from proof of concept to production on private/public cloud environments. Experience with scaling Robotics workloads in one or more areas, such as multimodal model training, inference, robot learning and simulation, large scale data processing and generation. Strong hands-on experience designing, deploying, and operating Kubernetes-based platforms for distributed GPU and AI workloads. Expertise in networking (DNS, LB, TCP/IP, firewalls), storage technology, workflow orchestration softwares (Airflow, Argo, etc), modern DevOps practices (GitOps, IaC, Observability), and orchestrating efficient GPU workloads Excellent communication skills to convey technical concepts to diverse audiences. Ways to stand out from the crowd: Hands-on experience with robotics frameworks (e.g., ROS2) and NVIDIA simulation and AI platforms such as Isaac Lab, Isaac Sim, GR00T or Cosmos. Previous exposure to large scale Robotics data curation, annotation, filtering pipelines, including the use of AI models for data labeling. Experience deploying NVIDIA inference technologies (Dynamo, NIM, Triton, vLLM) using acceleration techniques like quantization. Proficiency using and developing agentic workflows to accelerate software development, infrastructure automation, troubleshooting, and deployment workflows. Broad technical expertise across networking, compute, and storage systems (e.g., S3, NFS, Lustre), with hands-on experience building and debugging APIs (REST, gRPC). Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD