Used Tools & Technologies
Not specified
Required Skills & Competences ?
Python @ 5 Machine Learning @ 3 Data Analysis @ 5 System Architecture @ 3 PyTorch @ 5 Pandas @ 5 GPU @ 3Details
NVIDIA is a world leader in providing energy-efficient high-performance products and invests in the research and development of hyper-efficient GPU and SOC architectures. The Applied Power Architecture team develops state-of-the-art GPUs powering AI, HPC, Automotive, GeForce, and Mobile products.
Responsibilities
- Contribute to power estimation models and tools for GPU products and systems like NVIDIA DGX.
- Explore early GPU & System Architecture focusing on energy efficiency and TCO improvements at GPU and Datacenter level.
- Assist with Performance vs Power Analysis for NVIDIA's future product lineup.
- Deploy machine learning techniques to develop highly 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 hardware/software features for performance per watt improvements.
- Model and analyze cutting-edge technologies like high speed and high-density interconnects.
Requirements
- Pursuing MSEE/MSCE or equivalent experience related to Power / Performance estimation and optimization.
- Knowledge of energy-efficient chip design fundamentals and 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 preferred), system-software architectures, and performance/power modeling techniques.
- Proficiency in Python and data analysis packages: Pandas, NumPy, PyTorch.
- Familiarity with performance monitors/simulators used in modern processor architectures.
Benefits
- Base salary range from 108,000 USD to 212,750 USD, variable by location, experience, and similar pay scales.
- Eligibility for equity and benefits.
- NVIDIA is committed to diversity and equal opportunity employment.