Solutions Architect, Robotics AI & Cloud-Native GPUs

NVIDIA Corporation; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Job summary

NVIDIA Corporation is seeking a hands-on Solutions Architect to design and scale Kubernetes-native environments for distributed Robotics workloads. You will help partners deploy GPU-accelerated Physical AI pipelines and drive data factories across ingestion, preprocessing, and training stages.

You’ll collaborate with product management, engineering, and customers to translate requirements into scalable cloud-native architectures, enhancing scheduling, storage access, and GPU utilization in

Qualifications

• BS in CS/CE or equivalent experience.
• 5+ years in Solution Architecture or Infrastructure Engineering for AI/ML systems on private/public cloud.
• Experience scaling Robotics workloads: multimodal training, inference, sim.
• Hands-on designing, deploying, and operating Kubernetes-based platforms for distributed GPU/AI workloads.
• Expertise in networking, storage, orchestration (Airflow, Argo), GitOps, IaC, Observability, GPU workloads.

Responsibilities

• Help partners build scalable, observable, GPU-accelerated Physical AI pipelines.
• Support development of Physical AI data factories: ingestion, preprocessing, annotation, filtering, synthetic data, training, simulation, evaluation.
• Translate customer requirements into optimized cloud-native architectures for scheduling, cost, storage, and networking on hybrid infra.
• Accelerate distributed inference using NVIDIA technologies like NIM, TensorRT-LLM, vLLM, SGLang.
• Collaborate with business, engineering, and product teams; mentor customers implementing Physical AI at scale.

Skills

Kubernetes architecture

AI/ML systems

DevOps / GitOps

Observability

GPU workloads

REST/gRPC APIs

Education

BS in CS/CE or equivalent

Tools

Airflow

Argo

Job description

NVIDIA Corporation is seeking a hands-on Solutions Architect to design and scale Kubernetes-native environments for distributed Robotics workloads. You will help partners deploy GPU-accelerated Physical AI pipelines and drive data factories across ingestion, preprocessing, and training stages.

You’ll collaborate with product management, engineering, and customers to translate requirements into scalable cloud-native architectures, enhancing scheduling, storage access, and GPU utilization in