Listed on NVIDIA’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads performance strategy and optimization for NVIDIA's AI GPU clusters and networking infrastructure, overseeing end-to-end testing, analysis, and observability across distributed training and inference systems. It suits experienced systems engineers and research leaders with deep expertise in high-performance networking, RDMA, and collective communication who can bridge hardware, firmware, and software teams.
Our summary, not NVIDIA’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Software Engineering, or equivalent technical experience
- 8+ years of experience in High Performance Networking, RDMA, and systems-level performance
- 3+ years leading technical performance or R&D engineering teams
- Hands-on experience analyzing and optimizing collective communication (NCCL, MPI) for large-scale distributed AI workloads
- Experience designing and deploying Grafana dashboards for cluster monitoring and visualization
- Cross-team leadership and communication skills across hardware, software, and architecture groups
Nice to have
- Track record optimizing NCCL, RDMA/RoCEv2, and custom collective algorithms for multi-thousand GPU deployments running LLMs or MoE architectures
- Experience tuning advanced network traffic mechanisms like adaptive routing, PFC/ECN congestion control, and packet-spraying technologies
- Experience building autonomous performance-driven tools, AI-assisted root cause analysis agents, or automated regression frameworks
- Hands-on experience developing custom Grafana plugins, complex dashboard panels, or integrated alert management workflows