Senior Solutions Architect, Robotics Infrastructure

NVIDIA • Us, CA

2d

About The Position

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 AI/ML 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!

Requirements

• 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 VLM/VLA model training, model inference, robot learning and simulation, data generation.
• Strong 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 using the NVIDIA GPU Operator and MIG.
• Excellent communication skills to convey technical concepts to diverse audiences.
• BS in Computer Science, Computer Engineering, or a related field, or equivalent experience.

Nice To Haves

• Proficiency with robotics frameworks (e.g., ROS2) and NVIDIA simulation and AI platforms such as Isaac Lab, Isaac Sim or Cosmos.
• Experience with AI/ML training workflows and distributed job orchestration using tools like Ray.
• Deep expertise of transformer networks and experience deploying NVIDIA inference technologies (Dynamo, NIM, Triton, vLLM) using acceleration techniques like quantization.
• Experience with large scale data curation techniques and optimization
• Broad technical expertise across networking, compute, and storage systems (e.g., S3, NFS, Lustre), with hands-on experience building and debugging APIs (REST, gRPC) as well as r elevant certifications such as NVIDIA Certified AI Engineer, Certified Kubernetes Administrator (CKA), or Cloud Solutions Architect.

Responsibilities

• Support customers in building scalable and observable GPU-accelerated pipelines for key robotics workloads using Kubernetes, cloud-native technologies, and NVIDIA frameworks (OSMO, Dynamo) across heterogeneous infrastructure.
• Develop a deep understanding of robotics workloads scaling and help translate those into optimal architectures for partners
• Collaborate with DevOps teams to orchestrate data preprocessing, distributed training and inference workloads to optimize job scheduling, costs, storage access, and networking across hybrid and multi-cloud Kubernetes environments (e.g., AWS, Azure, GCP, on-prem).
• Accelerate inference pipelines using NVIDIA NIM, TensorRT-LLM, vLLM, SGLang, and other engines to enable seamless, disaggregated inference architectures.
• Collaborate with multi-functional teams (business, engineering, product) and provide technical mentorship to customers implementing Physical AI at scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees