Nvidia Senior Engineering Manager – AI Research Clusters 

United States, Texas 

361955560

What you'll be doing:

• Lead the design and deployment of scalable storage systems optimized for AI workloads and high-performance compute clusters.

• Drive readiness and operational enablement for upcoming hardware platforms, ensuring seamless integration and performance.

• Coordinate the development of internal tools to enhance storage provisioning, usage traceability, and user self-service.

• Guide the evaluation and implementation of new technologies to improve efficiency, reliability, and observability.

• Collaborate with cross-functional teams to align storage architecture with GPU cluster requirements and evolving research needs.

• Improve storage monitoring and metrics infrastructure to surface key insights and enable proactive management.

• Find opportunities to modernize existing storage systems for improved quota management, compression, and automation.

What we need to see:

• BS or equivalent experience.

• 12+ overall years of relevant technical experience.

• 5+ years of leadership experience.

• Proven ability to lead engineering teams building infrastructure at scale, especially in environments combining storage and high-performance computing.

• Deep technical knowledge in distributed storage systems, with experience improving data access patterns and platform observability.

• Familiarity with infrastructure deployment lifecycle – from planning and vendor engagement to rollout and operational readiness.

• Strong understanding of aligning storage performance with compute needs, and measuring system behavior based on real-world metrics.

• Ability to guide teams through technology evaluations, balancing technical rigor with speed and pragmatism.

Ways to stand out from the crowd:

• Experience with large-scale storage and networking systems inperformance-sensitiveenvironments such as HPC, AI, or scientific computing.

• Success in building tools or automation for self-service, visibility, and governance in complex infrastructure environments.

• Background in data observability and metrics correlation for infrastructure performance, cost efficiency, or capacity forecasting.

• Leading teams through cross-functional technical evaluations or RFPs, turning those into successful infrastructure deployments.

• Contributions to storage architecture improvements, including filesystem tuning, resource quota management, or data compression strategies.