Senior GPU Memory Architect

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1

NVIDIA is seeking a Senior GPU Memory Architect to define and architect features for next-generation GPU memory and on-chip interconnect subsystems. The role involves developing and refining performance models, analyzing benchmarks, and collaborating with engineering teams to optimize performance, power, and area (PPA).

What you'd actually do

  1. Define and architect innovative features for next-generation GPU memory and on-chip interconnect subsystems.
  2. Develop, implement, and refine performance models to evaluate architectural choices and predict subsystem behavior.
  3. Develop and evaluate test cases to validate performance models and ensure robust feature integration.
  4. Analyze benchmarks, application workloads, and performance/power simulation and emulation results to identify areas for architecture optimizations.
  5. Collaborate closely with multi-disciplinary engineering teams to translate product requirements into architectural solutions.

Skills

Required

  • Bachelor’s degree (or equivalent experience) in Computer Engineering, Electrical Engineering, Computer Science, or a related field with a minimum of 8+ years of relevant professional experience is required. Or an MS degree with 6+ years or a PhD with 4+ years of experience are also eligible.
  • An understanding in CPU or GPU architecture, memory systems, or network on-chip design.
  • Experience in large-scale SW development projects and strong programming skills in C/C++ and Python or other scripting languages.
  • Excellent communication skills, both written and verbal, for effective collaboration with internal teams and external partners.

Nice to have

  • Background in parallel computing, datacenter architecture, or large scale interconnect architecture.
  • Expertise in data analysis and visualization using tools such as pandas and related technologies.
  • Experience in writing, running, and analyzing test cases within performance modeling frameworks.
  • Experience in using AI tools for code development, validation, and analysis.

What the JD emphasized

  • minimum of 8+ years of relevant professional experience is required
  • An understanding in CPU or GPU architecture, memory systems, or network on-chip design.
  • Excellent communication skills, both written and verbal, for effective collaboration with internal teams and external partners.