Senior Customer Success Engineer - DGX Cloud

Company: Nvidia

Location: US, CA, Santa Clara (Remote)

Salary: $200k - $322k per year

Type: Full-time

Remote: Yes

Posted: 2026-07-29

About this role

NVIDIA’s DGX Cloud team helps some of the most advanced AI builders in the world move from idea to production faster. This Customer Success Manager role sits close to internal customers and acts as a strategic partner across onboarding, adoption, workload performance, and long-term platform success. The work is part customer success, part technical consulting, and part operational problem-solving, making it a strong fit for candidates who enjoy translating complex infrastructure into practical outcomes.

Customer Success Managers are typically most effective when they help customers achieve value quickly, build long-term trust, and bring customer insights back into the business to improve the product experience. This role is especially compelling because it combines customer partnership with cloud architecture, AI infrastructure, and resource planning in an environment where the work directly shapes how NVIDIA teams build and scale. Clear, familiar job titles and concise qualification lists also improve job-posting effectiveness and help attract the right candidates.gainsight

What you’ll be doing:

  • Partner with internal customers across the full lifecycle of their DGX Cloud usage, helping teams adopt the platform, remove blockers, and get faster time to value.zendesk
  • Translate workload, business, and technical requirements into scalable recommendations across compute, networking, storage, and cloud environments.
  • Turn repeat customer needs into reusable playbooks, tooling, dashboards, and operating patterns that improve adoption and reduce friction.gainsight
  • Work across Engineering, Product, Operations, and Finance to surface customer insights, influence roadmap decisions, and improve infrastructure planning and GPU resource efficiency.gainsight+1
  • Guide customers through architecture reviews, performance tuning, and cloud portability decisions for AI and machine learning workloads.
  • Use data, usage signals, and operational trends to identify risks...

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