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NVIDIA

Senior Applied Power Architect - GPU

Posted 20 Days Ago
Be an Early Applicant
In-Office
2 Locations
168K-311K Annually
Senior level
In-Office
2 Locations
168K-311K Annually
Senior level
The Senior Applied Power Architect - GPU will innovate methodologies for electrical challenges in GPU architecture, drive power management, and analyze power metrics for performance efficiency.
The summary above was generated by AI

NVIDIA is known as a world leader in providing energy-efficient high-performance products, and we continue to invest in the research and development of hyper-efficient GPU and SOC architectures. We are continually innovating in creative and unrivaled ways to improve our ability to deliver exceptional perf/watt solutions in a wide range of sectors and verticals. Come join NVIDIAs Applied Power Architecture team to develop state of the art GPUs to power AI, Automotive, GeForce, and Mobile products.

We are looking for a Senior Applied Power Architect – GPU.

What you'll be doing:

  • You will be responsible for inventing methodologies, features and techniques to address electrical/speed/power challenges for NVIDIA's next generation AI architectures such as Rubin and Feynman.

  • Drive power management initiatives to optimize power efficiency and performance, while addressing electrical/thermal and power constraints.

  • Understand process characteristics and its impact of V-F and drive analysis of various V-F tradeoffs considering Vt choices on power/other metrics and overall performance.

  • Investigate multi-die technologies and come up with innovative solutions to address packaging, interconnect, physical design, electrical and thermal challenges.

  • Analyze peak current, Di/Dt, IR and EM requirements, and collaborate closely with ASIC designers, Power Integrity, Packaging experts and Product engineers to understand challenges and formulate solutions.

What we need to see:

  • MSEE/MSCE, preferably PhD, or equivalent experience with a specialization in low-power-processor architectures.

  • Solid understanding of energy efficient system design fundamentals, performance/power modeling and related tradeoffs from your 6+ years of relevant industry experience.

  • A background in PDN design and good understanding of electrical issues such as Di/Dt, undershoot/overshoot, IR, EM, peak current, analysis/mitigation techniques are helpful.

  • Exposure and working knowledge of Python and data analysis packages like: Pandas, NumPy, PyTorch is a plus.

  • Exposure to lab setup including power measurement equipment such as scope/DAQ. Your ability to analyze board-level power issues like supply voltage, over-current etc. is helpful.

  • Strong interpersonal and organizational skills and the ability & desire to work as a great teammate.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. Our products are leading the way with groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. We have some of the most forward-thinking and hardworking people in the world working for us. Do you love the challenge of crafting the fastest and most power-efficient chips in their class? If so, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 29, 2025.NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Top Skills

Numpy
Pandas
Python
PyTorch

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