Power Methodology and Modeling Engineer - New College Grad 2026

NVIDIA NVIDIA · Semiconductors · Austin, TX

This role focuses on energy modeling for NVIDIA's next-generation GPUs, CPUs, and Tegra SoCs. The engineer will develop and implement tools and methodologies for data generation, sanitization, and integration with performance tools. They will also experiment with ML techniques to inform design decisions and improve power efficiency, working closely with various engineering teams. While ML is used, the core craft is not building ML models but applying them to power and energy analysis in chip design.

What you'd actually do

  1. Define and implement tools and methodologies for efficient data generation from post layout netlists to feed into data movement power analytical model.
  2. Develop tools and infrastructure to sanitize each metric in the model to achieve high correlation accuracy.
  3. Define and implement tools and methodologies for efficient integration of power models with performance tools.
  4. Identify runtime and memory limitation of existing flows and tools to speedup model delivery process.
  5. Mine data from pre- and post-silicon performance runs to find important data paths and bottlenecks. Give feedback to design teams and improve power efficiency.

Skills

Required

  • Python
  • C++
  • VLSI
  • digital design
  • computer architecture concepts
  • power and energy consumption
  • estimation
  • low power design
  • chip design process

Nice to have

  • machine learning
  • AI
  • statistical modeling

What the JD emphasized

  • energy modeling techniques
  • energy usage
  • power management
  • power analytical model
  • power models
  • power efficiency
  • power/energy targets
  • power and energy consumption