- 8+ years of experience building or operating production services and large-scale distributed systems, including hands-on automation.
- Strong programming skills in Python, Go, or a comparable language, with experience developing tools for production operations.
- Experience with infrastructure as code, configuration management, or GitOps, and with building automation for repeatable service deployments and changes.
- Strong knowledge of Linux, Kubernetes, containers, cloud infrastructure, distributed systems, and networking fundamentals; ability to diagnose failures in production.
- Understanding of Production Engineering principles, including SLIs, SLOs, error budgets, incident response, and reducing operational toil.
- Experience instrumenting services and using metrics, logs, and traces to understand system behavior and improve reliability.
- Clear technical communication and ability to work across engineering teams.
- BS/MS in Computer Science or equivalent practical experience.
Ways to stand out from the crowd
- Familiarity with technologies such as vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, or NCCL, and with GPU performance analysis.
- Experience building Kubernetes operators, controllers, workload orchestration services, fleet management systems, or self-healing automation.
- Experience with Terraform, Argo CD, CI/CD, policy validation, or safe deployment and rollback systems.
- Background with developing with AI tools and agents.
- Experience with production AI inference or agentic workloads, including debugging issues across models, runtimes, Kubernetes, and hardware.
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hard-working people on the planet working for us. If you're creative, hard-working and self-motivated, we want to hear from you!