GPU Power Architect - New College Grad 2026

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a New College Grad Datacenter GPU Power Architect to contribute to the research and development of energy-efficient GPU and SOC architectures. The role involves developing power estimation models and tools, exploring energy efficiency at GPU and Datacenter levels, and deploying machine learning techniques to model GPU, CPU, Switch, and platform performance and power. The candidate will understand GenAI/HPC workload characteristics to drive HW/SW features for Perf@Watt improvements.

What you'd actually do

  1. You will be contributing to power estimation models and tools for GPU products and systems like NVIDIA DGX.
  2. Early GPU & System Architecture exploration with focus on energy efficiency and TCO improvements at GPU and Datacenter level.
  3. You will help with Performance vs Power Analysis for NVIDIA future product lineup.
  4. Deploy machine learning techniques to develop highly accurate power and performance models of our GPUs, CPUs, Switches, and platforms.
  5. Understand the workload characteristics for GenAI/HPC workloads at Datacenter Scale (multi-GPU) to drive new HW/SW features for Perf@Watt improvements.

Skills

Required

  • Bachelors or Masters in Electrical Engineering, Computer Engineering, or equivalent experience
  • Knowledge of energy efficient chip design fundamentals and related tradeoffs
  • Familiarity with low power design techniques such as multi-VT, Clock gating, Power gating, and Dynamic Voltage-Frequency Scaling (DVFS)
  • Understanding of processors (GPU is a plus), system-SW architectures, and their performance/power modeling techniques
  • Proficiency with Python and data analysis packages like: Pandas, NumPy, PyTorch
  • Familiarity with performance monitors/simulators used in modern processor architectures

Nice to have

  • GPU is a plus

What the JD emphasized

  • energy efficient
  • power estimation models
  • performance models
  • GenAI/HPC workloads

Other signals

  • GPU architecture
  • Power efficiency
  • AI workloads
  • Machine learning models for power estimation