Principal Engineer, Performance Analysis - AI Applications and Services

$272,000 - $419,750/Yr

Nvidia - Santa Clara, CA

Full-time - Senior

Santa Clara, CA

Computer and Electronic Product Manufacturing

About the position

The Principal Engineer for Performance Analysis in AI Applications and Services will focus on optimizing distributed cloud-native accelerated video analytics applications. This role involves working closely with application teams to profile, identify bottlenecks, and enhance performance across CPU, GPU, and network accelerators in a Kubernetes environment. The engineer will drive performance initiatives, develop strategies for optimization, and standardize performance measurement processes to ensure efficient resource utilization and application performance.

Responsibilities

• Plan, enable, and drive performance initiatives across Cloud Native application teams.
• Review, develop, deploy, and manage tools and strategies to systematically run performance experiments.
• Collect and organize performance data with key partners.
• Work closely with application teams to understand application resource utilization characteristics and identify performance issues through profiling.
• Learn and understand various accelerators in the system for application workloads and recommend end-to-end performance optimizations.
• Assist developers and product teams on the best accelerators and systems for end-to-end system performance.
• Improve and standardize performance measurement processes across applications and GPU systems.
• Collaborate with GPU cloud-native teams at Nvidia to deploy optimal GPU resource sharing strategies in a Kubernetes environment.

Requirements

• Masters degree or PhD in Computer Science or a related field, or equivalent experience.
• 15+ years of experience in optimizing system design, complexity analysis, software design in Unix/Linux systems, performance, and application issues.
• Experience in real-time streaming AI inference systems.
• A history of working on distributed accelerated systems and solving sophisticated performance problems.
• Deep hands-on experience with distributed systems based on Kubernetes.
• Experience with on-prem and cloud systems and ability to work with partners across multiple teams.
• Experience using and handling and optimizing modern cloud and container-based enterprise computing architectures.
• Strong verbal and written communication and teamwork skills.
• Ability to multitask effectively in a multifaceted environment and action-driven with strong analytical skills.

Nice-to-haves

• Background with real-time computer vision AI inference and/or analytics platforms.
• Experience in application issues, algorithms, and data structures.
• Understanding of the functioning of AI services, deep learning, and AI.
• Exposure to scheduling and resource management systems.
• Knowledge of GPU programming such as OpenCL or CUDA and knowledge of multi-node GPU setups, GPU clusters, or cloud computing.

Benefits

• Equity options
• Comprehensive health benefits
• Flexible work hours
• Diversity and inclusion programs
• Professional development opportunities