Capacity Operations Manager

Role Overview

• Coordinate the development of High Performance Computing (HPC) clusters, collaborating closely with internal and external engineering teams.
• Direct and improve GPU capacity and additional compute resources across diverse cloud service platforms to satisfy rising needs and secure efficient deployment.
• Design, improve, and manage data models, reporting platforms, data automation solutions, dashboards, and performance measures that back NVIDIA Infrastructure governance programs and strategic capacity decisions.
• Assess the technical and business requirements for GPU capacity and other compute resources from different internal and external groups.
• Identify performance bottlenecks in day-to-day usage of compute resources and collaborate with relevant infrastructure teams to resolve them.
• Drive infrastructure resource efficiency initiatives in partnership with engineering, finance, and product teams.
• Develop and enhance tooling for our cloud infrastructure and analytics platform to optimize resource usage and performance for NVIDIA and its customers.
• This includes crafting and developing tools for automating workflows and potentially bringing to bear AI techniques to extract useful signals and insights from generated data.
• Partner and cross-collaborate with Finance, Product, Service Owners, and Infrastructure Engineering teams to align cloud capacity management with company goals and develop Infrastructure and Service Level benchmarks to match Customer satisfaction.

Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
• 8+ years of overall experience in cloud computing, specifically in managing or using GPU capacity for high performance computing.
• A proven record of large-scale computing operations and planning is a plus.
• Strong technical proficiency in cloud architecture, development and deployment, and managing large data sets.
• Experience with command line interfaces and shell scripting languages.
• Comprehensive knowledge of cloud service models (IaaS, PaaS, SaaS) and cloud infrastructure technologies.
• Practical experience with Cloud Service Providers including AWS, Azure, GCP, and OCI is essential.
• Demonstrated experience in bringing to bear AI tools and techniques to extract useful signals and insights from data, specifically to improve resource usage and automation.
• Deep knowledge and active use of statistical modeling and machine learning approaches for boosting operational efficiency and supporting strategic capacity decisions.
• Understanding of analytics, statistical modeling, and machine learning methodologies.
• Strong communication and relationship-building skills, with the ability to work well across different departments and contribute to strategic decisions.
• Self-starter, self-motivated, focused, and self-sufficient, with a willingness to learn new challenges and adapt quickly in a dynamic environment.
• Ability to operate effectively amidst uncertainty and rapidly changing business conditions, with an agile approach and a commitment to ongoing improvement.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Shell Scripting

Benefits

• equity
• benefits