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.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 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 scaling robotics workloads (multimodal model training, inference, robot learning, simulation, large-scale data processing and generation).
- Hands-on experience designing, deploying, and operating Kubernetes-based platforms for distributed GPU and AI workloads.
- Expertise in networking (DNS, load balancers, TCP/IP, firewalls), storage technologies, workflow orchestration (Airflow, Argo), modern DevOps practices (GitOps, IaC, Observability), and orchestrating efficient GPU workloads.
- Excellent communication skills to convey technical concepts to diverse audiences.
- Hands-on experience with robotics frameworks (ROS2) and NVIDIA simulation/AI platforms (Isaac Lab, Isaac Sim, GR00T, Cosmos).
- Experience deploying NVIDIA inference technologies (Dynamo, NIM, Triton, vLLM) and using acceleration techniques like quantization.
- Experience with large-scale robotics data curation, annotation, filtering pipelines and use of AI models for data labeling.
- Proficiency with storage systems (S3, NFS, Lustre) and building/debugging APIs (REST, gRPC).
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
