Senior Solutions Architect, Physical AI Cloud

NVIDIA • Santa Clara, CA

11d • $152,000 - $287,500

About The Position

NVIDIA is seeking a hands-on Solutions Architect to help enterprises deploy and scale Physical AI workloads, including robotics simulation, synthetic data generation, multi-step model training, and inference. The role focuses on designing and scaling Kubernetes-native environments for distributed robotics workloads, with expertise in backend infrastructure, inference, and cloud-native applications. The architect will work with product management, engineering, business teams, and ecosystem partners to advance adoption of NVIDIA Physical AI technologies.

Requirements

• Bachelor of Science in Computer Science, Computer Engineering, or a related field, or equivalent experience.
• At least 5 years of experience in Solution Architecture or Infrastructure Engineering, advancing AI/ML systems from proof of concept to production on private or public cloud environments.
• Experience scaling robotics workloads in one or more areas, such as multimodal model training, inference, robot learning and simulation, or 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, load balancing, TCP/IP, and firewalls), storage technology, workflow orchestration software (such as Airflow or Argo), modern DevOps practices (GitOps, IaC, and observability), and orchestrating efficient GPU workloads.
• Excellent communication skills for conveying technical concepts to diverse audiences.

Nice To Haves

• Hands-on experience with robotics frameworks such as ROS2 and NVIDIA simulation and AI platforms such as Isaac Lab, Isaac Sim, GR00T, or Cosmos.
• Exposure to large-scale robotics data curation, annotation, and filtering pipelines, including the use of AI models for data labeling.
• Experience deploying NVIDIA inference technologies such as Dynamo, NIM, Triton, or vLLM using acceleration techniques such as 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 (such as S3, NFS, and Lustre), with hands-on experience building and debugging REST or gRPC APIs.

Responsibilities

• Help partners build scalable, observable, GPU-accelerated Physical AI pipelines using agentic workflows, cloud-native technologies, and NVIDIA frameworks such as OSMO.
• Support development of Physical AI data factories covering data ingestion, preprocessing, annotation, filtering, synthetic data generation, training, simulation, and evaluation.
• Understand 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.

Benefits

• Equity
• Benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior