We’re building the infrastructure that powers GR00T, NVIDIA’s general-purpose humanoid robotics platform. This is not a typical DevOps job. You’ll help engineer the cloud-native backend that drives simulation, synthetic data generation, multi-stage model training, and robotic deployment—all at massive scale. Our orchestration system, NVIDIA OSMO, is built to handle real-time robotics workflows in cloud environments across thousands of GPUs. We’re looking for a pragmatic Kubernetes-native backend and infrastructure engineer who excels in solving complex orchestration problems in distributed AI/ML systems.
What you’ll be doing:
Architect, develop, and deploy backend services supporting NVIDIA GR00T using Kubernetes and cloud-native technologies.
Collaborate with ML, simulation, and robotics engineers to deploy scalable, reproducible, and observable multi-node training and inference workflows.
Extend and maintain OSMO’s orchestration layers to support heterogeneous compute backends and robotic data pipelines.
Develop Helm charts, controllers, CRDs, and service mesh integrations to support secure and fault-tolerant system operation.
Implement microservices written in Go or Python that power GR00T task execution, metadata tracking, and artifact delivery.
Optimize job scheduling, storage access, and networking across hybrid and multi-cloud Kubernetes environments (e.g., OCI, Azure, on-prem).
Build tooling that simplifies deployment, debugging, and scaling of robotics workloads.
What we need to see:
BS, MS, or PhD degree in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience)
5+ years of work experience in DevOps, backend, or cloud infrastructure engineering.
Hands-on experience building and deploying microservices in Kubernetes-native environments.
Proficiency in Golang or Python, especially for backend systems and operators.
Experience with Helm, or other Kubernetes templating and config management tools.
Familiarity with GitOps workflows, observability stacks (e.g., Prometheus, Grafana), and container CI/CD pipelines.
Strong understanding of container networking, storage (e.g., PVCs, ephemeral), and scheduling.
Ways to stand out from the crowd:
Experience with ML training workflows, distributed job orchestration (e.g., MPI, Ray, Triton Inference Server).
Knowledge of robotics frameworks (e.g., ROS2) or simulation tools (e.g., Isaac Sim, Omniverse).
Background with GPU cluster management and scheduling across cloud providers.
Contributions to open-source Kubernetes projects or custom operators/controllers.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you are creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 235,750 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.You will also be eligible for equity and benefits.
Top Skills
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.”
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