21 hours ago
Remote, AustraliaSenior
Responsibilities
- Maintain large-scale computational and AI infrastructure, including monitoring, logging, workload orchestration, Kubernetes, and Linux job schedulers.
- Optimize production-ready Kubernetes container platforms integrated with enterprise networking and storage.
- Lead technical customer engagements, troubleshoot complex issues, and advocate for customer technical needs.
- Support research and enterprise proofs of concept to validate features, architectures, and upgrade approaches.
- Collaborate with regional and headquarters engineering, product, and sales teams to develop solutions for partners.
- Develop automation tooling for infrastructure deployment, management, monitoring, alerting, and self-service resource consumption.
- Create runbooks, onboarding materials, best-practice guides, and other documentation.
- Provide technical leadership and strategic guidance for assigned customer accounts on DevOps and platform architecture.
Requirements
- A BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Physics, Mathematics, or a related field.
- 5+ years of professional experience managing scalable cloud environments and working in automation engineering roles.
- Experience with HPC and AI technologies, including CPUs, GPUs, high-speed interconnects, and supporting software stacks.
- Hands-on experience managing HPC or AI clusters, including deployment, optimization, troubleshooting, networking, data center architecture, storage, and GPU-accelerated compute.
- Extensive Kubernetes experience covering container orchestration, resource scheduling, scaling, and integration with HPC environments.
- Deep Linux knowledge, including Red Hat/CentOS or Ubuntu, OS-level security, TCP, DHCP, DNS, and storage technologies such as Lustre, GPFS, ZFS, XFS, and Kubernetes storage.
- Proficiency in Python and Bash, configuration management, and infrastructure-as-code tools such as Ansible and Terraform.
- Experience with Grafana, Loki, Prometheus, monitoring, logging, and fault-tolerant systems.
- A strong background in solution architecture and consultative customer support.
- Preferred experience includes NVIDIA DGX, CUDA, GPU Operator, container-based microservices, Spectrum, InfiniBand, cluster storage and scheduling, CI/CD pipelines, RDMA, and RoCE.
Tech Stack
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.