Senior Solutions Architect, Physical AI Cloud

NVIDIA

Solutions Architect helping NVIDIA partners scale Physical AI robotics pipelines and inference on Kubernetes. Designing cloud-native GPU infrastructure, simulation, data factories, and distributed AI workloads.

Posted 8/10/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $152,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying scalable, GPU-accelerated Physical AI pipelines using NVIDIA frameworks and cloud-native technologies. Proficient in orchestrating AI/ML systems and robotics workloads while providing technical guidance and mentorship.

Highest-signal resume keywords

Solution ArchitectureKubernetes-Based PlatformsAI/ML Systems DeploymentNetworking ExpertiseWorkflow Orchestration Software

ATS Keywords

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Hard Skills

GPU UtilizationRobotics Workload ScalingCloud-Native ArchitecturesDistributed InferenceData ProcessingAI Model TrainingDevOps PracticesREST APIsGRPC APIsSynthetic Data Generation

Soft Skills

Excellent Communication Skills

Tools & Technologies

NVIDIA OSMOAirflowArgoNIMTensorRT-LLMVLLMIsaac LabIsaac SimDynamoTriton

Industry Keywords

Physical AIRobotics FrameworksData CurationAnnotation PipelinesHybrid Infrastructure

Tech Stack

Tools & technologies

AirflowCloudDNSFirewallsGRPCKubernetesNFSTCP/IP

About the role

Key responsibilities & impact

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.
Improve 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.
Provide technical guidance and mentorship to customers implementing Physical AI at scale.

Requirements

What you’ll need

BS in Computer Science, Computer Engineering, or a related field, or equivalent experience.
5+ years of experience in Solution Architecture or Infrastructure Engineering.
Experience advancing AI/ML systems from proof of concept to production on private/public cloud environments.
Experience scaling Robotics workloads in 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, including DNS, load balancing, TCP/IP, and firewalls.
Expertise in storage technology.
Expertise with workflow orchestration software such as Airflow and Argo.
Expertise in modern DevOps practices including GitOps, IaC, and observability.
Experience orchestrating efficient GPU workloads.
Excellent communication skills to convey technical concepts to diverse audiences.
Preferred: hands-on experience with robotics frameworks such as ROS2 and NVIDIA platforms such as Isaac Lab, Isaac Sim, GR00T, or Cosmos.
Preferred: exposure to large-scale Robotics data curation, annotation, and filtering pipelines, including AI models for data labeling.
Preferred: experience deploying NVIDIA inference technologies such as Dynamo, NIM, Triton, and vLLM using quantization.
Preferred: proficiency using and developing agentic workflows.
Preferred: broad technical expertise across networking, compute, and storage systems such as S3, NFS, and Lustre, with hands-on experience building and debugging REST and gRPC APIs.

Benefits

Comp & perks

Equity
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