Datacenter GPU Power Architect

at Nvidia

📍 Santa Clara, United States

USD 116,000-218,500 per year

MIDDLE

✅ On-site

Used Tools & Technologies

Not specified

Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Python @ 5 Machine Learning @ 3 Data Analysis @ 5 System Architecture @ 3 PyTorch @ 5 Pandas @ 5 GPU @ 3 AI @ 3 GenAI @ 3 HPC @ 3

Details

NVIDIA is a world leader in energy-efficient high-performance products. Join NVIDIA's Applied Power Architecture team to develop state-of-the-art GPUs that power AI, HPC, Automotive, GeForce, and Mobile products. This role focuses on improving Perf/Watt for GPUs and datacenter systems.

Responsibilities

Contribute to power estimation models and tools for GPU products and systems (e.g., NVIDIA DGX/HGX based datacenters).
Early GPU and system architecture exploration with focus on energy efficiency and TCO improvements at the GPU and datacenter level.
Perform performance vs. power analysis and track ASIC milestones for future product lineups.
Deploy machine learning techniques to develop accurate power and performance models of GPUs, CPUs, switches, and platforms.
Understand workload characteristics for GenAI/HPC workloads at datacenter scale (multi-GPU) to drive new HW/SW features for Perf@Watt improvements.
Model and analyze technologies such as high-speed and high-density interconnects.

Requirements

MSEE/MSCE or equivalent experience, with 2+ years of experience related to power/performance estimation and optimization techniques.
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-software architectures, and performance/power modeling techniques.
Proficiency with Python and data analysis packages such as Pandas, NumPy, and PyTorch.
Familiarity with performance monitors/simulators used in modern processor architectures.

Compensation

Base salary range (Level 2): 116,000 USD - 189,750 USD per year.
Base salary range (Level 3): 136,000 USD - 218,500 USD per year.
You will also be eligible for equity and benefits (see company benefits link). Applications accepted at least until January 13, 2026.