Senior Manager GPU and AI Architecture
@ NVIDIA

Hybrid
$425,500
Hybrid
Full Time
Posted 1 day ago

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Job Details

Overview

NVIDIA is seeking an experienced and innovative leader in GPU and AI architecture and systems performance to manage, mentor, and grow a world-class engineering team. In this role, you will shape NVIDIA's next-generation AI training platforms, advanced GPUs, and cutting-edge software stacks.

What You Will Be Doing

As a Senior Manager GPU and AI Architecture, you will:

  • Analyze and anticipate leading-edge AI training workloads to guide strategic decisions.
  • Develop performance models, simulation environments, and critical infrastructure.
  • Collaborate with cross-functional teams in hardware, software, and product management.
  • Advocate for innovative hardware, system, and software enhancements.
  • Lead and scale a high-performance engineering team, managing career growth and project delivery.

What We Want To See

The ideal candidate will have:

  • An MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or related fields.
  • Over 4 years of management experience and 10+ years of industry experience.
  • Proven expertise in GPU and AI accelerator architectures with hands-on AI training experience.
  • Solid performance analysis skills including profiling and evaluation of AI systems.
  • Experience in managing and expanding technical teams with strong interpersonal abilities.

Additional Information

NVIDIA is renowned for its forward-thinking culture and commitment to diversity. Competitive base salary determined by location and experience, with benefits and equity eligibility. Applications accepted until July 29, 2025.

Key skills/competency

GPU, AI, Architecture, Performance, Leadership, Simulation, Hardware, Software, Team Management, Innovation

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How to Get Hired at NVIDIA

🎯 Tips for Getting Hired

  • Customize your resume: Highlight GPU and AI expertise.
  • Research NVIDIA culture: Understand their innovation and team values.
  • Focus on leadership: Emphasize team management experiences.
  • Prepare examples: Provide details on performance analysis projects.

📝 Interview Preparation Advice

Technical Preparation

Review advanced GPU architectures and design principles.
Study AI training workload simulations and performance models.
Practice hands-on deep learning framework optimizations.
Familiarize with hardware-software integration tools.

Behavioral Questions

Describe your leadership approach effectively.
Explain team conflict resolution examples.
Discuss decision-making under pressure.
Share a success story in mentoring teams.

Frequently Asked Questions