Senior Solutions Architect, Physical AI Cloud

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).

You will also be eligible for equity and benefits.

This posting is for an existing vacancy. 

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