$148,000 – $235,750
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 involves validating NVIDIA's AI infrastructure systems through first-time deployment, running benchmarks and workloads on multi-GPU clusters to identify and resolve performance issues before customer handoff. It suits experienced systems engineers who combine deep Linux and HPC expertise with practical AI/ML infrastructure knowledge and enjoy collaborating across hardware, software, and product teams to operationalize large-scale AI systems.
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 degree or equivalent in Computer Science, Mathematics, Engineering, Physics, or related field
- 6+ years managing Linux-based systems in HPC, distributed systems, or AI/ML environments
- Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters
- Practical knowledge of NCCL
- Understanding of collective communication patterns (AllReduce, AllToAll)
- Familiarity with LLM training and/or inference workflows using PyTorch or TensorFlow
- Proficiency with Python and Shell/Bash scripting
- Experience with benchmarking (design, execution, and interpretation)
- Comfortable interpreting observability data (metrics, logs, dashboards) for troubleshooting
- Strong cross-functional communication skills
Nice to have
- Experience with AI factory or large-scale AI infrastructure build, deployment, or operations
- Background in HPC performance engineering, SRE, or GPU-accelerated systems performance analysis
- Familiarity with observability stacks (metrics/monitoring, logging, tracing systems) for large distributed systems
- Experience building automation and CI-style pipelines for benchmark validation at scale
- Demonstrated drive to use AI to solve practical problems and guide data-driven decisions