Senior Deep Learning Performance Architect

NVIDIA•Austin, CA

14d

About The Position

We are now looking for a Senior Deep Learning Performance Architect! NVIDIA is seeking outstanding Performance Analysis Architects to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications. What you'll be doing: Develop innovative HW architectures to extend the state of the art in parallel computing performance, energy efficiency and programmability. Benchmark and analyze AI workloads in single and multi-node configurations. Develop high level simulator and analysis tools in C++/Python. Evaluate PPA (performance, power, area) for hardware features and system-level architectural trade-offs. Work closely with peer architecture teams and product management to guide development of the products. Keep abreast with emerging trends and research in deep learning. Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. NVIDIA's GPUs run AI algorithms, simulating human intelligence, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world. Increasingly known as “the AI computing company”, NVIDIA wants you. Come, join our Deep Learning Architecture team, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly growing field!

Requirements

MS or PhD in a relevant discipline (Computer Science, Electrical Engineering, Computer Engineering, etc) or equivalent experience.
4+ years of experience in parallel computing architectures, interconnect fabrics and deep learning applications.
Background in GPU or Deep Learning ASIC architecture evaluation for training and/or inference.
Strong programming skills in Python and C++.

Nice To Haves

Solid fundamental knowledge in computer architecture and interconnect fabrics.
Understanding of modern transformer-based model architectures.
Ability to simplify and communicate rich technical concepts to non-technical audience.
Have a curious demeanor with excellent problem-solving skills.

Responsibilities

Develop innovative HW architectures to extend the state of the art in parallel computing performance, energy efficiency and programmability.
Benchmark and analyze AI workloads in single and multi-node configurations.
Develop high level simulator and analysis tools in C++/Python.
Evaluate PPA (performance, power, area) for hardware features and system-level architectural trade-offs.
Work closely with peer architecture teams and product management to guide development of the products.
Keep abreast with emerging trends and research in deep learning.