Remote Customer-Facing GPU Infrastructure Engineer

Vero; United States; On-site; USD 120,000 - 200,000; Full time

Benefits offered by this job

Share Options

401(k) with employer match

Company-paid Life Insurance

Flexible Spending Account

Flexible PTO

Remote US

Job summary

Vero seeks a GPU Infrastructure Engineer (Customer Facing) to serve as a technical authority for customers deploying and scaling next-generation NVIDIA Blackwell GPU infrastructure. You’ll help AI platforms run reliably from deployment through production, ensuring scalability and performance across large clusters.

Work with advanced GPU runtimes, containers, and distributed scheduling while advising customers on best practices for AI workloads.

Qualifications

• Customer-facing HPC/Solution Engineer experience required.
• Experience with NVIDIA Blackwell GPUs (B200/GB200/GB300).
• Familiarity with GPU runtimes, containers, and distributed scheduling.
• Knowledge of network paths, storage throughput, and system-level workload performance.
• Ability to diagnose issues across compute, orchestration, and cluster.
• Strong understanding of InfiniBand or RoCE in high-performance networks.

Responsibilities

• Implement and configure customer AI environments across large-scale GPU clusters.
• Support AI workloads as they scale across GPUs, nodes, and regions.
• Optimize inference and training workloads for performance and efficiency.
• Troubleshoot infrastructure, runtime, and performance issues.
• Advise customers on AI platform architecture and workload best practices.
• Customize and rebuild inference engine packages to meet customer requirements.
• Develop reusable onboarding tools, templates, and automation to improve deployment consistency.

Job description

Vero seeks a GPU Infrastructure Engineer (Customer Facing) to serve as a technical authority for customers deploying and scaling next-generation NVIDIA Blackwell GPU infrastructure. You’ll help AI platforms run reliably from deployment through production, ensuring scalability and performance across large clusters.

Work with advanced GPU runtimes, containers, and distributed scheduling while advising customers on best practices for AI workloads.