Senior Customer Success Engineer - DGX Cloud

About the role

The Senior Customer Success Engineer will work with internal customers to drive adoption and success of NVIDIA’s DGX Cloud platform. This role blends customer success, technical consulting, and operational problem‑solving to translate complex AI infrastructure into practical outcomes.

Key responsibilities

• Partner with internal customers throughout the DGX Cloud lifecycle, helping teams adopt the platform, remove blockers, and accelerate time‑to‑value.
• Translate workload, business, and technical requirements into scalable recommendations across compute, networking, storage, and cloud environments.
• Develop reusable playbooks, tooling, dashboards, and operating patterns to improve adoption and reduce friction.
• Collaborate with Engineering, Product, Operations, and Finance to surface customer insights, influence roadmap decisions, and improve GPU resource efficiency.
• Guide customers through architecture reviews, performance tuning, and cloud portability decisions for AI/ML workloads.
• Analyze usage signals and operational trends to identify risks, spot opportunities, and enhance platform support.
• Contribute lightweight automation or code to accelerate delivery and improve day‑to‑day operations.

Required profile

• BS or MS in Computer Science, Engineering, or a related field, or equivalent experience.
• 12+ years of experience in customer success, solutions architecture, cloud engineering, technical account management, or a similar customer‑facing infrastructure role.
• Strong hands‑on knowledge of cloud infrastructure, including compute, networking, storage, and deployment patterns across public cloud and on‑premises environments.
• Experience with distributed systems, Kubernetes, schedulers, or large‑scale platform infrastructure.
• Proven ability to translate customer goals into technical solutions and influence cross‑functional teams.
• Excellent written and verbal communication skills.

Required skills

• Cloud infrastructure (compute, networking, storage)
• Public cloud platforms and on‑premises environments
• Distributed systems
• Kubernetes
• Schedulers
• Data analysis and operational signal interpretation
• Automation scripting / lightweight coding